Journals
| 88. | Jan Dvořák; Filip Hácha; Gerasimos Arvanitis; David Podgorelec; Konstantinos Moustakas; Libor Váša Survey of Inter-Prediction Methods for Time-Varying Mesh Compression Journal Article In: Computer Graphics Forum, pp. e15278, 2025. @article{dvorak2025survey,Abstract Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field. |
| 87. | Ioannis Romanelis; Vlassis Fotis; Konstantinos Moustakas; Adrian Munteanu ExpPoint-MAE: Better Interpretability and Performance for Self-Supervised Point Cloud Transformers Journal Article In: IEEE Access, vol. 12, pp. 53565-53578, 2024. @article{10497601,In this paper we delve into the properties of transformers, attained through self-supervision, in the point cloud domain. Specifically, we evaluate the effectiveness of Masked Autoencoding as a pretraining scheme, and explore Momentum Contrast as an alternative. In our study we investigate the impact of data quantity on the learned features, and uncover similarities in the transformer’s behavior across domains. Through comprehensive visualizations, we observe that the transformer learns to attend to semantically meaningful regions, indicating that pretraining leads to a better understanding of the underlying geometry. Moreover, we examine the finetuning process and its effect on the learned representations. Based on that, we devise an unfreezing strategy which consistently outperforms our baseline without introducing any other modifications to the model or the training pipeline, and achieve state-of-the-art results in the classification task among transformer models. |
| 86. | Ioannis Romanelis; Vlassios Fotis; Athanasios Kalogeras; Christos Alexakos; Konstantinos Moustakas; Adrian Munteanu Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models Journal Article In: arXiv preprint arXiv:2408.06145, 2024. @article{romanelis2024efficient,A novel architecture for 3D generative modeling, focusing on point cloud data has been proposed. The developed model (Sparse Point-Voxel Diffusion - SPVD), employs a dual-branch U-Net architecture demonstrates superior performance in unconditional shape generation tasks, compared to existing non-diffusion generative approaches. The largest variant of SPVD achieves state-of-the-art results among diffusion models, operating at approximately 70% of the runtime of the previous leading model, PVD. In addition to generation, SPVD showcased remarkable performance in point cloud completion and super-resolution tasks, demonstrating its versatility and robustness across various 3D modeling applications. |
| 85. | Vlassis Fotis; Ioannis Romanelis; Georgios Mylonas; Athanasios Kalogeras; Konstantinos Moustakas PReP: Efficient context-based shape retrieval for missing parts Journal Article In: arXiv preprint arXiv:2410.14245, 2024. @article{fotis2024prep,A novel method has been developed for retrieving suitable parts in the point cloud domain, particularly when the specific part is unavailable. The method is termed the Part Retrieval Pipeline (PReP). It utilizes metric learning and a trained classification model to assess potential replacement parts from a database. The model uses a progressively difficult training process to learn to identify appropriate parts with limited parameters and low computational cost. It can be used in large-scale warehouses or inventories and in applications in the circular economy, where to quickly identify parts based on shape context alone. |
| 84. | Iliana Loi; Evangelia I. Zacharaki; Konstantinos Moustakas Machine Learning Approaches for 3D Motion Synthesis and Musculoskeletal Dynamics Estimation: A Survey Journal Article In: IEEE Transactions on Visualization and Computer Graphics, 2023. @article{loi3dmotion, |
| 83. | Evgenia Moustridi; Konstantinos Risvas; Konstantinos Moustakas Predictive Simulation of Single-Leg Landing Scenarios for ACL Injury Risk Factors Evaluation Journal Article In: Plos One, vol. 18, no. 3, pp. e0282186, 2023. @article{moustridiACL, |
| 82. | Dimitris Nikos Fakotakis; Stavros Nousias; Gerasimos Arvanitis; Evangelia I Zacharaki; Konstantinos Moustakas AI Sound Recognition on Asthma Medication Adherence: Evaluation With the RDA Benchmark Suite Journal Article In: IEEE Access, vol. 11, pp. 13810–13829, 2023. @article{fakotakis2023ai,Asthma is a common, usually long-term respiratory disease with negative impact on global society and economy. Treatment involves using medical devices (inhalers) that distribute medication to the airways and its efficiency depends on the precision of the inhalation technique. There is a clinical need for objective methods to assess the inhalation technique, during clinical consultation. Integrated health monitoring systems, equipped with sensors, enable the recognition of drug actuation, embedded with sound signal detection, analysis and identification from intelligent structures, that could provide powerful tools for reliable content management. Health monitoring systems equipped with sensors, embedded with sound signal detection, enable the recognition of drug actuation and could be used for effective audio content analysis. This paper revisits sound pattern recognition with machine learning techniques for asthma medication adherence assessment and presents the Respiratory and Drug Actuation (RDA) Suite ( https://gitlab.com/vvr/monitoring-medication-adherence/rda-benchmark ) for benchmarking and further research. The RDA Suite includes a set of tools for audio processing, feature extraction and classification procedures and is provided along with a dataset, consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep networks’ architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses on challenges and future tendencies. |
| 81. | Stavros Nousias; Gerasimos Arvanitis; Aris Lalos; Konstantinos Moustakas Deep saliency mapping for 3D meshes and applications Journal Article In: ACM Transactions on Multimedia Computing, Communications and Applications, vol. 19, no. 2, pp. 1–22, 2023. @article{nousias2023deep,Nowadays, three-dimensional (3D) meshes are widely used in various applications in different areas (e.g., industry, education, entertainment and safety). The 3D models are captured with multiple RGB-D sensors, and the sampled geometric manifolds are processed, compressed, simplified, stored, and transmitted to be reconstructed in a virtual space. These low-level processing applications require the accurate representation of the 3D models that can be achieved through saliency estimation mechanisms that identify specific areas of the 3D model representing surface patches of importance. Therefore, saliency maps guide the selection of feature locations facilitating the prioritization of 3D manifold segments and attributing to vertices more bits during compression or lower decimation probability during simplification, since compression and simplification are counterparts of the same process. In this work, we present a novel deep saliency mapping approach applied to 3D meshes, emphasizing decreasing the execution time of the saliency map estimation, especially when compared with the corresponding time by other relevant approaches. Our method utilizes baseline 3D importance maps to train convolutional neural networks. Furthermore, we present applications that utilize the extracted saliency, namely feature-aware multiscale compression and simplification frameworks. |
| 80. | Nikolaos Anatoliotakis; Giorgos Paraskevopoulos; George Michalakis; Isidoros Michalellis; Evangelia I Zacharaki; Panagiotis Koustoumpardis; Konstantinos Moustakas Dynamic Human–Robot Collision Risk Based on Octree Representation Journal Article In: Machines, vol. 11, no. 8, pp. 793, 2023. @article{anatoliotakis2023dynamic,The automation of manufacturing applications where humans and robots operate in a shared environment imposes new challenges for presenting the operator’s safety and robot’s efficiency. Common solutions relying on isolating the robots’ workspace from human access during their operation are not applicable for HRI. This paper presents an extended reality-based method to enhance human cognitive awareness of the potential risk due to dynamic robot behavior towards safe human–robot collaborative manufacturing operations. A dynamic and state-aware occupancy probability map indicating the forthcoming risk of human–robot accidental collision in the 3D workspace of the robot is introduced. It is determined using octrees and is rendered in a virtual or augmented environment using Unity 3D. A combined framework allows the generation of both static zones (taking into consideration the entire configuration space of the robot) and dynamic zones (generated in real time by fetching the occupancy data corresponding to the robot’s current configuration), which can be utilized for short-term collision risk prediction. This method is then applied in a virtual environment of the workspace of an industrial robotic arm, and we also include the necessary technical adjustments for the method to be applied in an AR setting. |
| 79. | Yang Gao; Honglin Yuan; Tao Ku; Remco C Veltkamp; Georgios Zamanakos; Lazaros Tsochatzidis; Angelos Amanatiadis; Ioannis Pratikakis; Aliki Panou; Ioannis Romanelis; others SHREC 2023: Point cloud change detection for city scenes Journal Article In: Computers & Graphics, 2023. @article{gao2023shrec,Localization and navigation are the two most important tasks for mobile robots, which require an up-to-date and accurate map. However, to detect map changes from crowdsourced data is a challenging task, especially from billions of points collected by 3D acquisition devices. Collecting 3D data often requires expensive data acquisition equipment and there are limited data sources to evaluate point cloud change detection. To address these issues, in this Shape Retrieval Challenge (SHREC) track, we provide a city-scene dataset with real and synthesized data to detect 3D point cloud change. The dataset consists of 866 pairs of object changes from 78 city-scene 3D point clouds collected by LiDAR and 845 pairs of object changes from 100 city-scene 3D point clouds generated by a high-fidelity simulator. We compare three methods on this benchmark. Evaluation results show that data-driven methods are the current trend in 3D point cloud change detection. Besides, the siamese network architecture is helpful to detect changes in our dataset. We hope this benchmark and comparative evaluation results will further enrich and boost the research of point cloud change detection and its applications. |
| 78. | Sarthak Pati; ...; Peter Zampakis; Vasileios Panagiotopoulos; Panagiotis Tsiganos; Sotiris Alexiou; Ilias Haliassos; Evangelia I. Zacharaki; Konstantinos Moustakas; ...; Christina Kalogeropoulou; Spyridon Bakas Federated learning enables big data for rare cancer boundary detection Journal Article In: Nature Communications, vol. 13, no. 1, pp. 7346, 2022, ISSN: 2041-1723. @article{Pati2022,Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (nþinspace=þinspace6,þinspace314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing. |
| 77. | Konstantinos Risvas; Dimitar Stanev; Lefteris Benos; Konstantinos Filip; Dimitrios Tsaopoulos; Konstantinos Moustakas Evaluation of anterior cruciate ligament surgical reconstruction through finite element analysis Journal Article In: Scientific Reports, vol. 12, no. 1, pp. 8044, 2022. @article{risvas2022evaluation,Anterior cruciate ligament (ACL) tear is one of the most common knee injuries. The ACL reconstruction surgery aims to restore healthy knee function by replacing the injured ligament with a graft. Proper selection of the optimal surgery parameters is a complex task. To this end, we developed an automated modeling framework that accepts subject-specific geometries and produces finite element knee models incorporating different surgical techniques. Initially, we developed a reference model of the intact knee, validated with data provided by the Open Knee(s) project. This helped us evaluate the effectiveness of estimating ligament stiffness directly from MRI. Next, we performed a plethora of “what-if” simulations, comparing responses with the reference model. We found that (a) increasing graft pretension and radius reduces relative knee displacement, (b) the correlation of graft radius and tension should not be neglected, (c) graft fixation angle of 20∘ can reduce knee laxity, and (d) single-versus double-bundle techniques demonstrate comparable performance in restraining knee translation. In most cases, these findings confirm reported values from comparative clinical studies. The numerical models are made publicly available, allowing for experimental reuse and lowering the barriers for meta-studies. The modeling approach proposed here can complement orthopedic surgeons in their decision-making. |
| 76. | A Kloukiniotis; A Papandreou; A Lalos; P Kapsalas; D-V Nguyen; K Moustakas Countering adversarial attacks on autonomous vehicles using denoising techniques: A review Journal Article In: IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 61–80, 2022. @article{kloukiniotis2022countering,The evolution of automotive technology will eventually permit the automated driving system on the vehicle to handle all circumstances. Human occupants will be just passengers. This poses security issues that need to be addressed. This paper has two aims. The first one investigates strategies for robustifying scene analysis of adversarial road scenes. A taxonomy of the defense mechanisms for countering adversarial perturbations is initially presented, classifying those mechanisms in three major categories: those that modify the data, those that propose adding extra models, and those that focus on modifying the models deployed for scene analysis. Motivated by the limited number of surveys in the first category, we further analyze the approaches that utilize input transformation operations as countermeasures, further classifying them in supervised and unsupervised methods and highlighting both their strengths and weaknesses. The second aim of this paper is to publish CarlaScenes dataset produced using the CARLA simulator. An extensive evaluation study, on CarlaScenes, is performed testing the supervised deep learning approaches that have been either proposed for image restoration or adversarial noise removal. The study presents insights on the robustness of the aforementioned approaches in mitigating adversarial attacks in scene analysis operations. |
| 75. | Evangelos Chatzikalymnios; Konstantinos Moustakas Landing site detection for autonomous rotor wing UAVs using visual and structural information Journal Article In: Journal of Intelligent & Robotic Systems, vol. 104, no. 2, pp. 27, 2022. @article{chatzikalymnios2022landing,The technology of unmanned aerial vehicles (UAVs) has increasingly become part of many civil and research applications in recent years. UAVs offer high-quality aerial imaging and the ability to perform quick, flexible and in-depth data acquisition over an area of interest. While navigating in remote environments, UAVs need to be capable of autonomously landing on complex terrains for security, safety and delivery reasons. This is extremely challenging as the structure of these terrains is often unknown, and no prior knowledge can be leveraged. In this study, we present a vision-based autonomous landing system for rotor wing UAVs equipped with a stereo camera and an inertial measurement unit (IMU). The landing site detection algorithm introduces and evaluates several factors including terrain’s flatness, inclination and steepness. Considering these features we compute map metrics that are used to obtain a landing-score map, based on which we detect candidate landing sites. The 3D reconstruction of the scene is acquired by stereo processing and the pose of the UAV at any given time is estimated by fusing raw data from the inertial sensors with the pose obtained from stereo ORB-SLAM2. Real-world trials demonstrate successful landing in unknown and complex terrains such as suburban and forest areas. |
| 74. | Aggeliki Anastasiou; Evangelia I Zacharaki; Anastasios Tsakas; Konstantinos Moustakas; Dimitris Alexandropoulos Laser fabrication and evaluation of holographic intrinsic physical unclonable functions Journal Article In: Scientific Reports, vol. 12, no. 1, pp. 2891, 2022. @article{anastasiou2022laser,Optical Physical Unclonable Functions (PUFs) are well established as the most powerful anticounterfeiting tool. Despite the merits of optical PUFs, widespread use is hindered by existing implementations that are complicated and expensive. On top, the overwhelming majority of optical PUFs refer to extrinsic implementations. Here we overcome these limitations to demonstrate for the first time strong intrinsic optical PUFs with exceptional security characteristics. In doing so, we use Computer-Generated Holograms (CGHs) as optical, intrinsic, and image-based PUFs. The required randomness is offered by the non-deterministic fabrication process achieved with industrial friendly, nanosecond pulsed fiber lasers. Adding to simplicity and low cost, the digital fingerprint is derived by a setup which is designed to be adjustable in a production line. In addition, we propose a novel signature encoding and authentication mechanism that exploits manifold learning techniques to efficiently differentiate data reconstruction-related variation from counterfeit attacks. The proposed method is applied experimentally on silver plates. The robustness of the fabricated intrinsic optical PUFs is evaluated over time. The results have shown exceptional values for robustness and a probability of cloning up to |
| 73. | Gerasimos Damigos; Evangelia I Zacharaki; Nefeli Zerva; Angelos Pavlopoulos; Konstantina Chatzikyrkou; Argyro Koumenti; Konstantinos Moustakas; Constantinos Pantos; Iordanis Mourouzis; Athanasios Lourbopoulos Machine learning based analysis of stroke lesions on mouse tissue sections Journal Article In: Journal of Cerebral Blood Flow & Metabolism, vol. 42, no. 8, pp. 1463–1477, 2022. @article{damigos2022machine,An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named "StrokeAnalyst", which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2-2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models. |
| 72. | Chiara Romanengo; Andrea Raffo; Silvia Biasotti; Bianca Falcidieno; Vlassis Fotis; Ioannis Romanelis; Eleftheria Psatha; Konstantinos Moustakas; Ivan Sipiran; Quang-Thuc Nguyen; others SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds Journal Article In: Computers & Graphics, vol. 107, pp. 32–49, 2022. @article{romanengo2022shrec,This paper presents the methods that have participated in the SHREC 2022 track on the fitting and recognition of simple geometric primitives on point clouds. As simple primitives we mean the classical surface primitives derived from constructive solid geometry, i.e., planes, spheres, cylinders, cones and tori. The aim of the track is to evaluate the quality of automatic algorithms for fitting and recognizing geometric primitives on point clouds. Specifically, the goal is to identify, for each point cloud, its primitive type and some geometric descriptors. For this purpose, we created a synthetic dataset, divided into a training set and a test set, containing segments perturbed with different kinds of point cloud artifacts. Among the six participants to this track, two are based on direct methods, while four are either fully based on deep learning or combine direct and neural approaches. The performance of the methods is evaluated using various classification and approximation measures. |
| 71. | K. Kalatzis A. Chrysanthakopoulou; K. Moustakas Immersive Virtual Reality Experience of Historical Events Using Haptics and Locomotion Simulation Journal Article In: Applied Sciences, vol. 11, no. 24, pp. 11613, 2021. @article{immersive2021, |
| 70. | C. Vitale; N. Piperigkos; C. Laoudias; G. Ellinas; J. Casademont; J. Escrig; A. Kloukiniotis; A. S. Lalos; K. Moustakas; R. D. Rodriguez; D. Baños; G. R. Crusats; P. Kapsalas; K. P. Hofmann; P. S. Khodashenas CARAMEL: Results on a Secure Architecture for Connected and Autonomous Vehicles Detecting GPS Spoofing Attacks Journal Article In: Eurasip Journal on Wireless Communications and Networking, vol. vol. 115, 2021. @article{Vitale2021,The main goal of the H2020-CARAMEL project is to address the cybersecurity gaps introduced by the new technological domains adopted by modern vehicles applying, among others, advanced Artificial Intelligence and Machine Learning techniques. As a result, CARAMEL enhances the protection against threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture aiming at assessing the integrity of the information transmitted by vehicles, as well as at improving the security and privacy of communication for connected and autonomous driving. The proposed architecture includes: (1) multiradio access technology capabilities, with simultaneous 802.11p and LTE-Uu support, enabled by the connectivity infrastructure; (2) a MEC platform, where, among others, algorithms for detecting attacks are implemented; (3) an intelligent On-Board Unit with anti-hacking features inside the vehicle; (4) a Public Key Infrastructure that validates in real-time the integrity of vehicle’s data transmissions. As an indicative application, the interaction between the entities of the CARAMEL architecture is showcased in case of a GPS spoofing attack scenario. Adopted attack detection techniques exploit robust in-vehicle and cooperative approaches that do not rely on encrypted GPS signals, but only on measurements available in the CARAMEL architecture. |
| 69. | E. Zacharaki K. Filip; K. Moustakas Regularized Multi-Structural Shape Modeling of the Knee Complex based on Deep Functional Maps Journal Article In: Computerized Medical Imaging and Graphics, vol. vol. 89, 2021. @article{Filip2021,The incorporation of a-priori knowledge on the shape of anatomical structures and their variation through Statistical Shape Models (SSMs) has shown to be very effective in guiding highly uncertain image segmentation problems. In this paper, we construct multiple-structure SSMs of purely geometric nature, that describe the relationship between adjacent anatomical components through Canonical Correlation Analysis. Shape inference is then conducted based on a regularization term on the shape likelihood providing more reliable structure representations. A fundamental prerequisite for performing statistical shape analysis on a set of objects is the identification of corresponding points on their associated surfaces. We address the correspondence problem using the recently proposed Functional Maps framework, which is a generalization of point-to-point correspondence to manifolds. Additionally, we show that, by incorporating techniques from the deep learning theory into this framework, we can further enhance the ability of SSMs to better capture the shape variation in a given dataset. The efficiency of our approach is illustrated through the creation of 3D models of the human knee complex in two application scenarios: incomplete or noisy shape reconstruction and missing structure estimation. |
| 68. | D. Serpanos A.S Lalos P. Kapsalas; K. Moustakas The Role of Modularity in Multimodal Simultaneous Localization and Mapping Systems Journal Article In: IEEE Computer, vol. vol. 54, pp. 63-67, 2021. @article{Kapsalas2021,Simultaneous localization and mapping (SLAM) refers to the problem of mapping an environment using measurements from mobile sensors while simultaneously estimating the motion of those sensors relative to the map Modular architectures are required to enable the commoditization and fast penetration of SLAMs in the emerging mobile computing systems. |
| 67. | D. Stanev; K. Filip; D. Bitzas; S. Zouras; G. Giarmatzis; D. Tsaopoulos; K. Moustakas Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-based Solutions in Rehabilitation Journal Article In: Sensors, vol. vol. 21:5, 2021. @article{Stanev2021,This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge. |
| 66. | Aris S. Lalos; Gerasimos Arvanitis; Konstantinos Moustakas Robust and Fast 3D Saliency Mapping for Industrial Modeling Applications Journal Article In: IEEE Transactions on Industrial Informatics, vol. vol. 17, no. no. 1, pp. 1307-1317, 2021. @article{Arvanitis2021,New generation 3D scanning technologies are expected to create a revolution at the Industry 4.0, facilitating a large number of virtual manufacturing tools and systems. Such applications require the accurate representation of physical objects and/or systems achieved through saliency estimation mechanisms that identify certain areas of the 3D model, leading to a meaningful and easier to analyze representation of a 3D object. 3D saliency mapping is, therefore, guiding the selection of feature locations and is adopted in a large number of low-level 3D processing applications including denoising, compression, simplification and registration. In this work, we propose a robust and fast method for creating 3D saliency maps that accurately identifies sharp and small scale geometric features in various industrial 3D models. An extensive experimental study using a large number of 3D scanned and CAD models, verifies the effectiveness of the proposed method as compared to other recent and relevant approaches despite the constraints posed by complex geometry patterns or the presence of noise. |
| 65. | A. S. Lalos G. Arvanitis S. Nousias; K. Moustakas Fast Mesh Denoising with Data Driven Normal Filtering using Deep Variational Autoencoders Journal Article In: IEEE Transactions on Industrial Informatics, vol. vol. 17, no. no. 1, pp. 980-990, 2021. @article{Nousias2021,Recent advances in 3D scanning technology have enabled the deployment of 3D models in various industrial applications like digital twins, remote inspection and reverse engineering. Despite their evolving performance, 3D scanners,still introduce noise and artifacts in the acquired dense models.In this work, we propose a fast and robust denoising method for dense 3D scanned industrial models. The proposed approach employs conditional variational autoencoders to effectively filter face normals. Training and inference are performed in a sliding patch setup reducing the size of the required training data and execution times. We conducted extensive evaluation studies using 3D scanned and CAD models. The results verify plausible denoising outcomes, demonstrating similar or higher reconstruction accuracy, compared to other state-of-the-art approaches.Specifically, for 3D models with more than1e4faces, the presented pipeline is twice as fast as methods with equivalent reconstruction error. |
| 64. | D. Stanev I. Loi; K. Moustakas Total Knee Replacement: Subject-Specific Modeling, Finite Element Analysis and Evaluation of Dynamic Activities Journal Article In: Frontiers in Bioengineering and Biotechnology, vol. vol. 9, pp. 196, 2021. @article{Loi2021,This study presents a semi-automatic framework to create subject-specific total knee replacement finite element models, which can be used to analyze locomotion patterns and evaluate knee dynamics. In recent years, much scientific attention was attracted to pre-clinical optimization of customized total knee replacement operations through computational modeling to minimize post-operational adverse effects. However, the time-consuming and laborious process of developing a subject-specific finite element model poses an obstacle to the latter. One of this work's main goals is to automate the finite element model development process, which speeds up the proposed framework and makes it viable for practical applications. This pipeline's reliability was ratified by developing and validating a subject-specific total knee replacement model based on the 6th SimTK Grand Challenge data set. The model was validated by analyzing contact pressures on the tibial insert in relation to the patient's gait and analysis of tibial contact forces, which were found to be in accordance with the ones provided by the Grand Challenge data set. Subsequently, a sensitivity analysis was carried out to assess the influence of modeling choices on tibial insert's contact pressures and determine possible uncertainties on the models produced by the framework. Parameters, such as the position of ligament origin points, ligament stiffness, reference strain, and implant-bone alignment were used for the sensitivity study. Notably, it was found that changes in the alignment of the femoral component in reference to the knee bones significantly affect the load distribution at the tibiofemoral joint, with an increase of 206.48% to be observed at contact pressures during 5° internal rotation. Overall, the models produced by this pipeline can be further used to optimize and personalize surgery by evaluating the best surgical parameters in a simulated manner before the actual surgery. |
| 63. | M. Pavlou; D. Laskos; E. I. Zacharaki; K. Risvas; K. Moustakas XRSISE: An XR Training System for Interactive Simulation and Ergonomics Assessment Journal Article In: Frontiers in Virtual Reality, vol. vol. 2, pp. 17, 2021. @article{Pavlou2021,The use of virtual reality (VR) techniques for industrial training provides a safe and cost effective solution that contributes to increased engagement and knowledge retention levels. However, the process of experiential learning in a virtual world without biophysical constraints might contribute to muscle strain and discomfort, if ergonomic risk factors are not considered in advance. Under this scope, we have developed a digital platform which employs extended reality (XR) technologies for the creation and delivery of industrial training programs, by taking into account the users and workplace specificities through the adaptation of the 3D virtual world to the real environment. Our conceptual framework is composed of several inter-related modules: 1) the XR tutorial creation module, for automatic recognition of the sequence of actions composing a complex scenario while this is demonstrated by the educator in VR, 2) the XR tutorial execution module, for the delivery of visually guided and personalized XR training experiences, 3) the digital human model (DHM) based simulation module for creation and demonstration of job task simulations avoiding the need of an actual user and 4) the biophysics assessment module for ergonomics analysis given the input received from the other modules. Three-dimensional reconstruction and aligned projection of the objects situated in the real scene facilitated the imposition of inherent physical constraints, thereby allowed to seamlessly blend the virtual with the real world without losing the sense of presence. |
| 62. | Gerasimos Kougianos; Konstantinos Moustakas Large-scale ray traced water caustics in real-time using cascaded caustic maps Journal Article In: Computers & Graphics, vol. 98, pp. 255–267, 2021. @article{kougianos2021large,Achieving interactivity for applications with physically-accurate caustics is proven to be a challenging task. We present a hybrid method utilizing ray tracing and rasterization which enables water caustic coverage to vast view distance in real-time. Inspired by photon mapping, we optimize the generation of photons using cascaded caustic maps to avoid tracing the first bounce of a ray from a light, replacing that step with rasterization. We introduce cascades as a set of caustic maps with varying resolution based on the distance from the viewer. In addition, since we adopt a splatting approach where each photon is rasterized into the image based on the extent of its contribution, we trace photon differentials in order to determine the size, shape and intensity of the splats so as to achieve adaptive anisotropic flux density estimation. Finally, to mitigate undersampled regions where lack of photons leads to noise, we propose the use of a cross-bilateral filter with an adaptive kernel radius. The radius is based on the perceived radiant energy of a photon relative to the scene’s luminance, and drastically improves performance. We demonstrate how the use of our method is able to perform interactive rendering of large-scale dynamic water caustics. |
| 61. | Nikos Fazakis; Otilia Kocsis; Elias Dritsas; Sotiris Alexiou; Nikos Fakotakis; Konstantinos Moustakas Machine learning tools for long-term type 2 diabetes risk prediction Journal Article In: IEEE Access, vol. 9, pp. 103737–103757, 2021. @article{fazakis2021machine,A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to health-related issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, well-being and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) databas... |
| 60. | Andrea Raffo; Ulderico Fugacci; Silvia Biasotti; Walter Rocchia; Yonghuai Liu; Ekpo Otu; Reyer Zwiggelaar; David Hunter; Evangelia I Zacharaki; Eleftheria Psatha; others SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties Journal Article In: Computers & Graphics, vol. 99, pp. 1–21, 2021. @article{raffo2021shrec,This paper presents the methods that have participated in the SHREC 2021 contest on retrieval and classification of protein surfaces on the basis of their geometry and physicochemical properties. The goal of the contest is to assess the capability of different computational approaches to identify different conformations of the same protein, or the presence of common sub-parts, starting from a set of molecular surfaces. We addressed two problems: defining the similarity solely based on the surface geometry or with the inclusion of physicochemical information, such as electrostatic potential, amino acid hydrophobicity, and the presence of hydrogen bond donors and acceptors. Retrieval and classification performances, with respect to the single protein or the existence of common sub-sequences, are analysed according to a number of information retrieval indicators. |
| 59. | Tao Ku; Sam Galanakis; Bas Boom; Remco C Veltkamp; Darshan Bangera; Shankar Gangisetty; Nikolaos Stagakis; Gerasimos Arvanitis; Konstantinos Moustakas SHREC 2021: 3D point cloud change detection for street scenes Journal Article In: Computers & Graphics, vol. 99, pp. 192–200, 2021. @article{ku2021shrec,The rapid development of 3D acquisition devices enables us to collect billions of points in a few hours. However, the analysis of the output data is a challenging task, especially in the field of 3D point cloud change detection. In this Shape Retrieval Challenge (SHREC) track, we provide a street-scene dataset for 3D point cloud change detection. The dataset consists of 866 3D object pairs in year 2016 and 2020 from 78 large-scale street scene 3D point clouds. Our goal is to detect the changes from multi-temporal point clouds in a complex street environment. We compare three methods on this benchmark, with one handcrafted (PoChaDeHH) and the other two learning-based (HGI-CD and SiamGCN). The results show that the handcrafted algorithm has balanced performance over all classes, while learning-based methods achieve overwhelming performance but suffer from the class-imbalanced problem and may fail on minority classes. The randomized oversampling metric applied in SiamGCN can alleviate this problem. Also, different siamese network architecture in HGI-CD and SiamGCN contribute to the designing of a network for the 3D change detection task. |
| 58. | D. Stanev E. Zacharaki F. Nikolopoulos; K. Moustakas Personalized Knee Geometry Modelling based on Multi-Atlas Segmentation and Mesh Refinement Journal Article In: IEEE Access, vol. vol.8, no. no. 1, pp. 56766-65781, 2020. @article{Nikolopoulos2020b,The development of personalized finite element models of the knee anatomy is critically important in the simulation of knee joint mechanics, prediction of optimal treatments in cases of pathological conditions and prevention of injuries. Subject-specific models can be obtained from diagnostic images with multi-atlas segmentation being a pertinent choice when prior anatomical information of the structures of interest is available. Although multi-atlas segmentation has been prevalent in some parts of the body, its exploitation for the segmentation of the knee complex has not been illustrated yet. This work utilizes a multi-atlas segmentation method based on deformable registration and joint label fusion in conjunction with anatomically-adopted mesh refinement in order to generate subject-specific models of the knee. The success of finite element simulations strongly depends on the properties of the 3D surface and the quality of the volumetric meshes. Therefore, emphasis was given to create structured meshes with well-shaped hexahedra for the knee cartilages and menisci. The segmentation performance is assessed using cross-validation on7 subjects from the Open Knee project and 78 subjects from the Osteoarthritis Initiative. Our results indicate that our developed state-of-the-art processing scheme can achieve competitive performance, opening the path for better diagnostics and patient-specific interventions. The developed tools are freely available to download from SimTK at https://simtk.org/projects/knee-segment |
| 57. | E. I. Zacharaki G. Giarmatzis; K. Moustakas Real-time prediction of joint forces by motion capture and machine learning Journal Article In: Sensors 2020, 2020. @article{Giarmatzis2020,Conventional biomechanical modelling approaches involve the solution of large systems of equations that encode the complex mathematical representation of human motion and skeletal structure. To improve stability and computational speed, being a common bottleneck in current approaches, we apply machine learning to train surrogate models and to predict in near real-time,previously calculated medial and lateral knee contact forces (KCFs) of 54 young and elderly participants during treadmill walking in a speed range of 3 to 7 km/h. Predictions are obtained by fusing optical motion capture and musculoskeletal modeling-derived kinematic and force variables, into regression models using artificial neural networks (ANNs) and support vector regression (SVR). Training schemes included either data from all subjects (LeaveTrialsOut) or only from a portion of them (LeaveSubjectsOut),in combination with inclusion of ground reaction forces (GRFs) in the dataset or not. Results identify ANNs as the best-performing predictor of KCFs, both in terms of PearsonR(0.89–0.98 for LeaveTrialsOut and 0.45–0.85 for LeaveSubjectsOut) and percentage normalized root mean square error (0.67–2.35forLeaveTrialsOutand 1.6–5.39 for LeaveSubjectsOut). When GRFs were omitted from the dataset,no substantial decrease in prediction power of both models was observed. Our findings showcase the strength of ANNs to predict simultaneously multi-component KCF during walking at different speeds—even in the absence of GRFs—particularly applicable in real-time applications that make use of knee loading conditions to guide and treat patients. |
| 56. | Stavros Nousias; Gerasimos Arvanitis; Aris S. Lalos; George Pavlidis; Christos Koulamas; Athanasios Kalogeras; Konstantinos Moustakas A Saliency Aware CNN-based 3D model Simplification and Compression Framework for Remote Inspection of Heritage Sites Journal Article In: IEEE Access, vol. 8, pp. 16998-170001, 2020. @article{Nousias2020b,Nowadays, the preservation and maintenance of historical objects is the main priority in the area of the heritage culture. The new generation of 3D scanning devices and the new assets of technological improvements have created a fertile ground for developing tools that could facilitate challenging tasks which traditionally required a huge amount of human effort and specialized knowledge of experts (e.g., a detailed inspection of defects in a historical object due to aging). These tasks demand more human effort, especially in some special cases, such as the inspection of a large-scale or remote object (e.g., tall columns, the roof of historical buildings, etc.), where the preserver expert does not have easy access to it. In this work, we propose a saliency aware compression and simplification framework for efficient remote inspection of Structure From Motion (SFM) reconstructed heritage 3D models. More specifically, we present a Convolutional Neural Network (CNN) based saliency map extraction pipeline that highlights the most important information of a 3D model.These include geometric details such as the fine features of the model or surface defects. An extensive experimental study, using a large number of real SFM reconstructed heritage 3D models, verifies the effectiveness and the robustness of the proposed method providing very promising results and draws future directions. |
| 55. | L. Benos; D. Stanev; L. Spyrou; K. Moustakas; D. E. Tsaopoulos A review on finite element modelling and simulation of the anterior cruciate ligament reconstruction Journal Article In: Frontiers in Bioengineering and Biotechnology, vol. vol. 8, pp. 967, 2020. @article{Benos2020b,The anterior cruciate ligament (ACL) constitutes one of the most important stabilizing tissues of the knee joint whose rapture is very prevalent. ACL reconstruction (ACLR) from a graft is a surgery which yields the best outcome. Taking into account the complicated nature of this operation and the high cost of experiments, finite element (FE) simulations can become a valuable tool for evaluating the surgery in a pre-clinical setting. The present study summarizes, for the first time, the current advancement in ACLR in both clinical and computational level. It also emphasizes on the material modeling and properties of the most popular grafts as well as modeling of different surgery techniques. It can be concluded that more effort is needed to be put toward more realistic simulation of the surgery, including also the use of two bundles for graft representation, graft pretension and artificial grafts. Furthermore, muscles and synovial fluid need to be included, while patellofemoral joint is an important bone that is rarely used. More realistic models are also required for soft tissues, as most articles used isotropic linear elastic models and springs. In summary, accurate and realistic FE analysis in conjunction with multidisciplinary collaboration could contribute to ACLR improvement provided that several important aspects are carefully considered. |
| 54. | Elia Moscoso Thompson; Silvia Biasotti; Andrea Giachetti; Claudio Tortorici; Naoufel Werghi; Ahmad Shaker Obeid; Stefano Berretti; Hoang-Phuc Nguyen-Dinh; Minh-Quan Le; Hai-Dang Nguyen; Minh-Triet Tran; Leonardo Gigli; Santiago Velasco-Forero; Beatriz Marcotegui; Ivan Sipiran; Benjamin Bustos; Ioannis Romanelis; Vlassis Fotis; Gerasimos Arvanitis; Konstantinos Moustakas; Ekpo Otu; Reyer Zwiggelaar; David Hunter; Yonghuai Liu; Yoko Arteaga; Ramamoorthy Luxman SHREC 2020: Retrieval of digital surfaces with similar geometric reliefs Journal Article In: Computers & Graphics, vol. Volume 91, pp. 199-281, 2020. @article{Thompson2020,This paper presents the methods that have participated in the SHREC’20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local ”geometric pattern” information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches. |
| 53. | Konstantinos Moustakas; Aris S. Lalos; Gerasimos Arvanitis Spectral Processing for Denoising and Compression of 3D Meshes using Dynamic Orthogonal Iterations Journal Article In: Journal of Imaging, 2020. @article{Arvanitis2020b,Recently, spectral methods have been extensively used in the processing of 3D meshes. They usually take advantage of some unique properties that the eigenvalues and the eigenvectors of the decomposed Laplacian matrix have. However, despite their superior behavior and performance, they suffer from computational complexity, especially while the number of vertices of the model increases. In this work, we suggest the use of a fast and efficient spectral processing approach applied to dense static and dynamic 3D meshes, which can be ideally suited for real-time denoising and compression applications. To increase the computational efficiency of the method, we exploit potential spectral coherence between adjacent parts of a mesh and then we apply an orthogonal iteration approach for the tracking of the graph Laplacian eigenspaces. Additionally, we present a dynamic version that automatically identifies the optimal subspace size that satisfies a given reconstruction quality threshold. In this way, we overcome the problem of the perceptual distortions, due to the fixed number of subspace sizes that is used for all the separated parts individually. Extensive simulations carried out using different 3D models in different use cases (i.e., compression and denoising), showed that the proposed approach is very fast, especially in comparison with the SVD based spectral processing approaches, while at the same time the quality of the reconstructed models is of similar or even better reconstruction quality. The experimental analysis also showed that the proposed approach could also be used by other denoising methods as a preprocessing step, in order to optimize the reconstruction quality of their results and decrease their computational complexity since they need fewer iterations to converge. |
| 52. | Evangelia I Zacharaki; Konstantinos Deltouzos; Spyridon Kalogiannis; Ilias Kalamaras; Luca Bianconi; Cristiana Degano; Roberto Orselli; Javier Montesa; Konstantinos Moustakas; Konstantinos Votis; Dimitrios Tzovaras; Vasileios Megalooikonomou FrailSafe: An ICT platform for unobtrusive sensing of multi-domain frailty for personalized interventions Journal Article In: IEEE Journal on Biomedical and Health Informatics, 2020. @article{Zacharaki2020b,The implications of frailty in older adults' health status and autonomy necessitates the understanding and effective management of this widespread condition as a priority for modern societies. Despite its importance, we still stand far from early detection, effective management and prevention of frailty. One of the most important reasons for this is the lack of sensitive instruments able to early identify frailty and pre-frailty conditions. The FrailSafe system provides a novel approach to this complex, medical, social and public health problem. It aspires to identify the most important components of frailty, construct cumulative metrics serving as biomarkers, and apply this knowledge and expertise for self-management and prevention. This paper presents a high-level overview of the FrailSafe system architecture providing details on the monitoring sensors and devices, the software front-ends for the interaction of the users with the system, as well as the back-end part including the data analysis and decision support modules. Data storage, remote processing and security issues are also discussed. The evaluation of the system by older individuals from 3 different countries highlighted the potential of frailty prediction strategies based on information and communication technology (ICT). |
| 51. | R. J. Khusial; P.J.Honkoop; O. Usmani; M. Sores; M. Biddiscombe A. Simpson; S. Meah; M. Bonini; A. Lalas; E. Polychronidou; J. G. Koopmans; K. Moustakas; J. B. Snoeck-Stroband; S. Ortmann; K. Votis; D. Tzovaras; K. F. Chung; S. Fowler; J. K. Sont Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma Journal Article In: The Journal of Allergy and Clinical Immunology: In Practice, 2020. @article{Khusial2020b,BACKGROUND: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. OBJECTIVE: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. METHODS: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. RESULTS: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P [ .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P [ .06). Asthmarelated quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P [ .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P [ .007) |
| 50. | Vaggelis Ntalianis; Nikos Dimitris Fakotakis; Stavros Nousias; Aris S Lalos; Michael Birbas; Evangelia I Zacharaki; Konstantinos Moustakas Deep CNN sparse coding for real time inhaler sounds classification Journal Article In: Sensors, 2020. @article{Ntalianis2020b,Effective management of chronic constrictive pulmonary conditions lies in proper and timely administration of medication. As a series of studies indicates, medication adherence can effectively be monitored by successfully identifying actions performed by patients during inhaler usage. This study focuses on the recognition of inhaler audio events during usage of pressurized metered dose inhalers (pMDI). Aiming at real-time performance, we investigate deep sparse coding techniques including convolutional filter pruning, scalar pruning and vector quantization, for different convolutional neural network (CNN) architectures. The recognition performance has been assessed on three healthy subjects following both within and across subjects modeling strategies. The selected CNN architecture classified drug actuation, inhalation and exhalation events, with 100%, 92.6% and 97.9% accuracy, respectively, when assessed in a leave-one-subject-out cross-validation setting. Moreover, sparse coding of the same architecture with an increasing compression rate from 1 to 7 resulted in only a small decrease in classification accuracy (from 95.7% to 94.5%), obtained by random (subject-agnostic) cross-validation. A more thorough assessment on a larger dataset, including recordings of subjects with multiple respiratory disease manifestations, is still required in order to better evaluate the method’s generalization ability and robustness. |
| 49. | K. Moustakas E.I. Zacharaki S. Nousias AVATREE: An open-source computational modeling framework modeling Anatomically Valid Airway Tree conformations Journal Article In: PLoS One, 2020. @article{Nousias2020,This paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making |
| 48. | Dimitrios Chamzas; Constantinos Chamzas; Konstantinos Moustakas cMinMax: A fast algorithm to find the corners of an N-dimensional convex polytope Journal Article In: arXiv preprint arXiv:2011.14035, 2020. @article{chamzas2020cminmax,During the last years, the emerging field of Augmented & Virtual Reality (AR-VR) has seen tremendousgrowth. At the same time there is a trend to develop low cost high-quality AR systems where computing poweris in demand. Feature points are extensively used in these real-time frame-rate and 3D applications, thereforeefficient high-speed feature detectors are necessary. Corners are such special features and often are used as thefirst step in the marker alignment in Augmented Reality (AR). Corners are also used in image registration andrecognition, tracking, SLAM, robot path finding and 2D or 3D object detection and retrieval. Therefore thereis a large number of corner detection algorithms but most of them are too computationally intensive for use inreal-time applications of any complexity. Many times the border of the image is a convex polygon. For thisspecial, but quite common case, we have developed a specific algorithm, cMinMax. The proposed algorithmis faster, approximately by a factor of 5 compared to the widely used Harris Corner Detection algorithm. Inaddition is highly parallelizable. The algorithm is suitable for the fast registration of markers in augmentedreality systems and in applications where a computationally efficient real time feature detector is necessary.The algorithm can also be extended to N-dimensional polyhedrons. |
| 47. | Aggeliki Anastasiou; Evangelia I Zacharaki; Dimitris Alexandropoulos; Konstantinos Moustakas; Nikolaos A Vainos Machine learning based technique towards smart laser fabrication of CGH Journal Article In: Microelectronic Engineering, vol. 227, pp. 111314, 2020. @article{anastasiou2020machine,Fabrication of Computer-Generated Holograms (CGHs) on metal surfaces is a challenging procedure, given the nature of the laser-matter interaction specified for metals, and the power requirements for silver laser machining. A machine learning approach is derived for engraving of CGHs on silver surfaces with a 1070 nm fiber laser. The proposed method paves the way towards an automated solution for the fabrication of CGH on silver surfaces that accounts for, in terms of manufacturability. Sophisticated image-based descriptors are extracted from digital holographic masks produced by commercial CGH design software to predict, using machine learning, a “quality score” from ‘1’ to ‘5’, estimating the fabrication feasibility of a CGH's mask. Based on this idea, the procedure of CGH engraving on silver is remarkably improved. |
| 46. | Ilja Gubins; Marten L Chaillet; Gijs Der Schot; Remco C Veltkamp; Friedrich Förster; Yu Hao; Xiaohua Wan; Xuefeng Cui; Fa Zhang; Emmanuel Moebel; others SHREC 2020: Classification in cryo-electron tomograms Journal Article In: Computers & Graphics, vol. 91, pp. 279–289, 2020. @article{gubins2020shrec,Cryo-electron tomography (cryo-ET) is an imaging technique that allows us to three-dimensionally visualize both the structural details of macro-molecular assemblies under near-native conditions and its cellular context. Electrons strongly interact with biological samples, limiting electron dose. The latter limits the signal-to-noise ratio and hence resolution of an individual tomogram to about 50 (5 nm). Biological molecules can be obtained by averaging volumes, each depicting copies of the molecule, allowing for resolutions beyond 4 (0.4 nm). To this end, the ability to localize and classify components is crucial, but challenging due to the low signal-to-noise ratio. Computational innovation is key to mine biological information from cryo-electron tomography. To promote such innovation, we provide a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in cryo-electron tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Six research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching, a traditional method widely used in cryo-ET research. We find that method performance correlates with particle size, especially noticeable for template matching which performance degrades rapidly as the size decreases. We learn that neural networks can achieve significantly better localization and classification performance, in particular convolutional networks with focus on high-resolution details such as those based on U-Net architecture. |
| 45. | Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas Adaptive Representation of 3D Meshes for Low-Latency Applications Journal Article In: Computer Aided Geometric Design, vol. 73, pp. 70-85, 2019. @article{Arvanitis2019adaptive,Recently, 3D visual representations of highly deformable 3D models, such as dynamic 3D meshes, are becoming popular due to their capability to represent realistically the motion of real-world objects/humans, paving the road for new and more advanced immersive virtual, augmented and mixed reality experiences. However, the real-time streaming of such models introduces increasing challenges related to low cost, low-latency and scalable coding of the acquired information. In view of this, this article proposes an efficient scalable coding mechanism, that decomposes a mesh sequence into spatial and temporal layers that remove a single vertex at each layer. The removed vertices are predicted by performing Laplacian interpolation of the motion vectors. The artifacts that are introduced in low-resolution representations are mitigated using a subspace based normal-vector denoising procedure, that is optimized to support low-latency streaming scenarios using incremental SVD. A novel initialization strategy offers robustness to outliers generated due to local deformations. An extensive evaluation study using several synthetic and scanned dynamic 3D meshes highlights the benefits of the proposed approach in terms of both execution time and reconstruction quality even in very low throughput scenarios of bit-per-vertex-per-frame (bpvf). |
| 44. | Aris S. Lalos; Gerasimos Arvanitis; Konstantinos Moustakas Denoising of Dynamic 3D Meshes via Low-Rank Spectral Analysis Journal Article In: Computers & Graphics, vol. 82, pp. 140-151, 2019. @article{Arvanitis2019denoising,Recently, the new generation of different 3D scanner devices (e.g., conoscopic holography, structured light, photometric systems, etc.) has attracted a lot of attention due to their ability to provide more reliable results. The easiness of capturing real 3D objects has created revolutionary trends in many areas (e.g., gaming, prominence of heritage, military, medicine, etc.) and has significantly increased the interest for static and dynamic 3D models. However, despite the technological evolution of the 3D acquisition devices, there are still limitations, deteriorating the quality of the generated results (e.g., noise, outliers, and other abnormalities). These issues need to be addressed before the 3D models are used by other applications (such as segmentation, object recognition, tracking, etc.). In this paper, we introduce a novel method which exploits similarities at the spectral frequencies of individual meshes in soft or rigid body 3D animations. The noise is mainly distributed over high frequencies, while the spectrum of the graph Fourier transform of sequential meshes in a 3D animation, exhibits a low-rank which can be effectively exploited using robust principal component analysis (RPCA). Extensive evaluation studies, carried out using a variety of different arbitrarily complex 3D animations and noise patterns, verify that the proposed technique achieves plausible denoising results despite the constraints posed by arbitrarily motion scenarios. |
| 43. | Dimitar Stanev; Konstantinos Moustakas Stiffness modulation of redundant musculoskeletal systems Journal Article In: Journal of Biomechanics, vol. 85, pp. 101 - 107, 2019, ISSN: 0021-9290. @article{Stanev2019g,This work presents a framework for computing the limbs’ stiffness using inverse methods that account for the musculoskeletal redundancy effects. The musculoskeletal task, joint and muscle stiffness are regulated by the central nervous system towards improving stability and interaction with the environment during movement. Many pathological conditions, such as Parkinson’s disease, result in increased rigidity due to elevated muscle tone in antagonist muscle pairs, therefore the stiffness is an important quantity that can provide valuable information during the analysis phase. Musculoskeletal redundancy poses significant challenges in obtaining accurate stiffness results without introducing critical modeling assumptions. Currently, model-based estimation of stiffness relies on some objective criterion to deal with muscle redundancy, which, however, cannot be assumed to hold in every context. To alleviate this source of error, our approach explores the entire space of possible solutions that satisfy the action and the physiological muscle constraints. Using the notion of null space, the proposed framework rigorously accounts for the effect of muscle redundancy in the computation of the feasible stiffness characteristics. To confirm this, comprehensive case studies on hand movement and gait are provided, where the feasible endpoint and joint stiffness is evaluated. Notably, this process enables the estimation of stiffness distribution over the range of motion and aids in further investigation of factors affecting the capacity of the system to modulate its stiffness. Such knowledge can significantly improve modeling by providing a holistic overview of dynamic quantities related to the human musculoskeletal system, despite its inherent redundancy. |
| 42. | Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas; Nikolaos Fakotakis Feature Preserving Mesh Denoising Based on Graph Spectral Processing Journal Article In: IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 3, pp. 1513-1527, 2019, ISSN: 2160-9306. @article{Arvanitis2019,The increasing interest for reliable generation of large scale scenes and objects has facilitated several real-time applications. Although the resolution of the new generation geometry scanners are constantly improving, the output models, are inevitably noisy, requiring sophisticated approaches that remove noise while preserving sharp features. Moreover, we no longer deal exclusively with individual shapes, but with entire scenes resulting in a sequence of 3D surfaces that are affected by noise with different characteristics due to variable environmental factors (e.g., lighting conditions, orientation of the scanning device). In this work, we introduce a novel coarse-to-fine graph spectral processing approach that exploits the fact that the sharp features reside in a low dimensional structure hidden in the noisy 3D dataset. In the coarse step, the mesh is processed in parts, using a model based Bayesian learning method that identifies the noise level in each part and the subspace where the features lie. In the feature-aware fine step, we iteratively smooth face normals and vertices, while preserving geometric features. Extensive evaluation studies carried out under a broad set of complex noise patterns verify the superiority of our approach as compared to the state-of-the-art schemes, in terms of reconstruction quality and computational complexity. |
| 41. | Aris S. Lalos; Gerasimos Arvanitis; Evangelos Vlachos; Konstantinos Moustakas; Kostas Berberidis Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications Journal Article In: IEEE Access, vol. 7, no. 1, pp. 15779-15803, 2019. @article{Lalos2019b,Nowadays, real-time 3D scanning and reconstruction becomes a requirement for a variety of interactive applications in various fields, including heritage science, gaming, engineering, landscape topography, and medicine. From the introduction of 3D scanning, which allowed the representation of real world or synthetic objects into the virtual world, hardware and software advances have seen tremendous progress. However, despite the continuous improvement of the new generation image sensors and acquisition techniques, the acquired data are often corrupted by the low-frequency noise, outliers, misalignment, missing data, and variations in point density. These effects are amplified if the low-cost sensors and hardware are being used (e.g., mobile devices); thus, the acquisition and communication cost per datum is driven to a minimum. This paper provides a comprehensive review of the ongoing efforts in geometry and signal processing, describing several models from a wide range of signal processing relevant tasks, such as robust principal component analysis, compressive sampling, and matrix completion. Various scalable architectures and optimization algorithms are analyzed and reviewed, revealing significant insights into the fundamental processing operations and the involved implementation tradeoffs. Moreover, the impact of sparse modeling and optimization tools to several 3D mesh processing tasks, such as completion of missing data, feature preserving noise removal, and rejection of outliers, is illustrated via test cases with several constraints posed by the arbitrarily complex animated scenarios. Finally, the identified limitations together with the potential open research directions are also presented for future research efforts toward modeling and optimization for static and dynamic 3D models |
| 40. | S. Kalogiannis; K. Deltouzos; E. I. Zacharaki; A. Vasilakis; K. Moustakas J. Ellul; V. Megalooikonomou Integrating an openEHR-based personalized virtual model for the ageing population within HBase Journal Article In: BMC Medical Informatics and Decision Making, vol. 19, no. 25, 2019. @article{Kalogiannis2019b,Background: Frailty is a common clinical syndrome in ageing population that carries an increased risk for adverse health outcomes including falls, hospitalization, disability, and mortality. As these outcomes affect the health and social care planning, during the last years there is a tendency of investing in monitoring and preventing strategies. Although a number of electronic health record (EHR) systems have been developed, including personalized virtual patient models, there are limited ageing population oriented systems. Methods: We exploit the openEHR framework for the representation of frailty in ageing population in order to attain semantic interoperability, and we present the methodology for adoption or development of archetypes. We also propose a framework for a one-to-one mapping between openEHR archetypes and a column-family NoSQL database (HBase) aiming at the integration of existing and newly developed archetypes into it. Results: The requirement analysis of our study resulted in the definition of 22 coherent and clinically meaningful parameters for the description of frailty in older adults. The implemented openEHR methodology led to the direct use of 22 archetypes, the modification and reuse of two archetypes, and the development of 28 new archetypes. Additionally, the mapping procedure led to two different HBase tables for the storage of the data. Conclusions: In this work, an openEHR-based virtual patient model has been designed and integrated into an HBase storage system, exploiting the advantages of the underlying technologies. This framework can serve as a base for the development of a decision support system using the openEHR’s Guideline Definition Language in the future |
| 39. | Dimitar Stanev; Konstantinos Moustakas Modeling musculoskeletal kinematic and dynamic redundancy using null space projection Journal Article In: PLOS ONE, vol. 14, no. 1, pp. 1-26, 2019. @article{Stanev2019f,The coordination of the human musculoskeletal system is deeply influenced by its redundant structure, in both kinematic and dynamic terms. Noticing a lack of a relevant, thorough treatment in the literature, we formally address the issue in order to understand and quantify factors affecting the motor coordination. We employed well-established techniques from linear algebra and projection operators to extend the underlying kinematic and dynamic relations by modeling the redundancy effects in null space. We distinguish three types of operational spaces, namely task, joint and muscle space, which are directly associated with the physiological factors of the system. A method for consistently quantifying the redundancy on multiple levels in the entire space of feasible solutions is also presented. We evaluate the proposed muscle space projection on segmental level reflexes and the computation of the feasible muscle forces for arbitrary movements. The former proves to be a convenient representation for interfacing with segmental level models or implementing controllers for tendon driven robots, while the latter enables the identification of force variability and correlations between muscle groups, attributed to the system’s redundancy. Furthermore, the usefulness of the proposed framework is demonstrated in the context of estimating the bounds of the joint reaction loads, where we show that misinterpretation of the results is possible if the null space forces are ignored. This work presents a theoretical analysis of the redundancy problem, facilitating application in a broad range of fields related to motor coordination, as it provides the groundwork for null space characterization. The proposed framework rigorously accounts for the effects of kinematic and dynamic redundancy, incorporating it directly into the underlying equations using the notion of null space projection, leading to a complete description of the system. |
| 38. | T. Vafeiadis; C. Ziogou; G. Stavropoulos; S. Krinidis; D. Ioannidis; S. Voutetakis; D. Tzovaras; K. Moustakas Early malfunction diagnosis of industrial process units utilizing online linear trend profiles and real-time classification Journal Article In: International Journal of Adaptive Control and Signal Processing, vol. 32, no. 9, 2018. @article{Vafeiadis2018,The early detection of potential malfunctions at process systems can significantly reduce downtime and improve their overall operability. In that context, this paper demonstrates the behavior and response, through a comparative analysis, of novel data-driven diagnosis methods for interdependent time series. The proposed real-time slope statistic profile method utilizes a self-adaptive sliding window based on a real-time classification technique of linear trend profiles of both interdependent time series and internal condition so as to avoid misdetections. The calculation of the linear trend profile is based on a standard parametric linear trend test, and the selection of possible incidents is based on its two-level cross-checking. All possible combinations for the calculation of the trend test and cross-checking are created to explore their efficiency. The proposed methods are tested against real data sets from a chemical process system of the Centre for Research and Technology Hellas/Chemical Process Energy and Resources Institute derived from specific scenarios during nominal operating conditions. |
| 37. | E. Vlachos; A. Lalos; A. Spathis; K. Moustakas Distributed Consolidation of Highly Incomplete Dynamic Point Clouds Based on Rank Minimization Journal Article In: IEEE Transactions on Multimedia, vol. 20, no. 12, pp. 3276 - 3288, 2018. @article{Vlachos2018b,Recently, there has been increasing interest for easy and reliable generation of 3-D animated models facilitating several real-time applications (like immersive telepresence, motion capture, and gaming). In most of these applications, the reconstruction of soft body animations is based on timevarying point clouds, which are nonuniformly sampled and highly incomplete. To overcome these significantly challenging imperfections without any additional information, first we introduce a novel reconstruction technique based on rank minimization theory, which can result in a unique solution to the otherwise ill-posed problem. This technique is further extended to exploit the spatial coherence, which usually characterizes the soft-body animations. Based on the developed tools, we propose a distributed consolidation technique where the reconstruction is performed by working simultaneously on several groups of frames. To achieve this, we impose temporal coherence between successive frame clusters by constraining the rank minimization problem. We validate the proposed techniques via experimental evaluation under different configurations and animated models, where we show that the high-frequency details of themodels can be adequately recovered from a highly incomplete geometry data set. |
| 36. | G. Arvanitis A.S. Lalos S.E. Nousias An mHealth system for monitoring medication adherence in obstructive respiratory diseases using content based audio classification Journal Article In: IEEE Access, vol. 6, pp. 11871-11882, 2018. @article{Nousias2018b,Asthma and chronic obstructive pulmonary disease are obstructive respiratory diseases that affect negatively the quality of life for patients and their families worldwide. Despite the significance of these diseases, their management has been considered suboptimal around the world, whereas the improper inhaler use has been underlined as one of the main causes. Toward this direction, this paper presents an integrated mHealth system that provides real-time personalized feedback to patients for assessing the proper medication use and educating them and helping them avoid common mistakes. The identification of proper inhaler use is based on conventional and data-driven feature extraction and classification methods employed for the identification of four events (inhaler actuation, inhalation, exhalation, and background noise). The proposed scheme reaches 98% classification accuracy significantly outperforming recent and relevant state-of-the-art approaches. Finally, intuitive feedback interfaces were implemented in the form of a virtual guidance agent integrated with the mobile application, which can help patients follow their action plan and assess their inhaler technique in a more engaging manner. Extensive simulation studies, carried out using 12 subjects, demonstrated the efficiency of the proposed approaches in both indoor and outdoor environments. |
| 35. | Sebastian S. James; Chris Papapavlou; Alexander Blenkinsop; Alexander J. Cope; Sean R. Anderson; Konstantinos Moustakas; Kevin N. Gurney Integrating Brain and Biomechanical Models—A New Paradigm for Understanding Neuro-muscular Control Journal Article In: Frontiers in Neuroscience, vol. 12, pp. 39, 2018, ISSN: 1662-453X. @article{James2018b,To date, realistic models of how the central nervous system governs behavior have been restricted in scope to the brain, brainstem or spinal column, as if these existed as disembodied organs. Further, the model is often exercised in relation to an in vivo physiological experiment with input comprising an impulse, a periodic signal or constant activation, and output as a pattern of neural activity in one or more neural populations. Any link to behavior is inferred only indirectly via these activity patterns. We argue that to discover the principles of operation of neural systems, it is necessary to express their behavior in terms of physical movements of a realistic motor system, and to supply inputs that mimic sensory experience. To do this with confidence, we must connect our brain models to neuro-muscular models and provide relevant visual and proprioceptive feedback signals, thereby closing the loop of the simulation. This paper describes an effort to develop just such an integrated brain and biomechanical system using a number of pre-existing models. It describes a model of the saccadic oculomotor system incorporating a neuromuscular model of the eye and its six extraocular muscles. The position of the eye determines how illumination of a retinotopic input population projects information about the location of a saccade target into the system. A pre-existing saccadic burst generator model was incorporated into the system, which generated motoneuron activity patterns suitable for driving the biomechanical eye. The model was demonstrated to make accurate saccades to a target luminance under a set of environmental constraints. Challenges encountered in the development of this model showed the importance of this integrated modeling approach. Thus, we exposed shortcomings in individual model components which were only apparent when these were supplied with the more plausible inputs available in a closed loop design. Consequently we were able to suggest missing functionality which the system would require to reproduce more realistic behavior. The construction of such closed-loop animal models constitutes a new paradigm of computational neurobehavior and promises a more thoroughgoing approach to our understanding of the brain's function as a controller for movement and behavior. |
| 34. | D Stanev; K Moustakas Simulation of Constrained Musculoskeletal Systems in Task Space Journal Article In: IEEE Transactions on Biomedical Engineering, vol. vol. 65, no. no. 2, pp. 307-318, 2018, ISSN: 1558-2531. @article{Stanev2018b,This work proposes an operational task space formalization of constrained musculoskeletal systems, motivated by its promising results in the field of robotics. Methods: The change of representation requires different algorithms for solving the inverse and forward dynamics simulation in the task space domain. We propose an extension to the Direct Marker Control and an adaptation of the Computed Muscle Control algorithms for solving the inverse kinematics and muscle redundancy problems respectively. Results: Experimental evaluation demonstrates that this framework is not only successful in dealing with the inverse dynamics problem, but also provides an intuitive way of studying and designing simulations, facilitating assessment prior to any experimental data collection. Significance: The incorporation of constraints in the derivation unveils an important extension of this framework towards addressing systems that use absolute coordinates and topologies that contain closed kinematic chains. Task space projection reveals a more intuitive encoding of the motion planning problem, allows for better correspondence between observed and estimated variables, provides the means to effectively study the role of kinematic redundancy and, most importantly, offers an abstract point of view and control, which can be advantageous towards further integration with high level models of the precommand level. Conclusion: Task-based approaches could be adopted in the design of simulation related to the study of constrained musculoskeletal systems. |
| 33. | Antonios Lalas; Stavros Nousias; Dimitrios Kikidis; Aris Lalos; Gerasimos Arvanitis; Christos Sougles; Konstantinos Moustakas; Konstantinos Votis; Sylvia Verbanck; Omar Usmani; Dimitrios Tzovaras Substance deposition assessment in obstructed pulmonary system through numerical characterization of airflow and inhaled particles attributes Journal Article In: BMC Medical Informatics and Decision Making, vol. 17, 2017. @article{Lalas2017b,Background: Chronic obstructive pulmonary disease (COPD) and asthma are considered as the two most widespread obstructive lung diseases, whereas they affect more than 500 million people worldwide. Unfortunately, the requirement for detailed geometric models of the lungs in combination with the increased computational resources needed for the simulation of the breathing did not allow great progress to be made in the past for the better understanding of inflammatory diseases of the airways through detailed modelling approaches. In this context, computational fluid dynamics (CFD) simulations accompanied by fluid particle tracing (FPT) analysis of the inhaled ambient particles are deemed critical for lung function assessment. Also they enable the understanding of particle depositions on the airways of patients, since these accumulations may affect or lead to inflammations. In this direction, the current study conducts an initial investigation for the better comprehension of particle deposition within the lungs. More specifically, accurate models of the airways obstructions that relate to pulmonary disease are developed and a thorough assessment of the airflow behavior together with identification of the effects of inhaled particle properties, such as size and density, is conducted. Our approach presents a first step towards an effective personalization of pulmonary treatment in regards to the geometric characteristics of the lungs and the in depth understanding of airflows within the airways. Methods: A geometry processing technique involving contraction algorithms is established and used to employ the different respiratory arrangements associated with lung related diseases that exhibit airways obstructions. Apart from the normal lung case, two categories of obstructed cases are examined, i.e. models with obstructions in both lungs and models with narrowings in the right lung only. Precise assumptions regarding airflow and deposition fraction (DF) over various sections of the lungs are drawn by simulating these distinct incidents through the finite volume method (FVM) and particularly the CFD and FPT algorithms. Moreover, a detailed parametric analysis clarifies the effects of the particles size and density in terms of regional deposition upon several parts of the pulmonary system. In this manner, the deposition pattern of various substances can be assessed. Results: For the specific case of the unobstructed lung model most particles are detected on the right lung (48.56% of total, when the air flowrate is 12.6 L/min), a fact that is also true when obstructions arise symmetrically in both lungs (51.45% of total, when the air flowrate is 6.06 L/min and obstructions occur after the second generation). In contrast, when narrowings are developed on the right lung only, most particles are pushed on the left section (68.22% of total, when the air flowrate is 11.2 L/min) indicating that inhaled medication is generally deposited away from the areas of inflammation. This observation is useful when designing medical treatment of lung diseases. Furthermore, particles with diameters from 1 μm to 10 μm are shown to be mainly deposited on the lower airways, whereas particles with diameters of 20 μm and 30 μm are mostly accumulated in the upper airways. As a result, the current analysis indicates increased DF levels in the upper airways when the particle diameter is enlarged. Additionally, when the particles density increases from 1000 Kg/m3 to 2000 Kg/m3, the DF is enhanced on every generation and for all cases investigated herein. The results obtained by our simulations provide an accurate and quantitative estimation of all important parameters involved in lung modeling. Conclusions: The treatment of respiratory diseases with inhaled medical substances can be advanced by the clinical use of accurate CFD and FPT simulations and specifically by evaluating the deposition of inhaled particles in a regional oriented perspective in regards to different particle sizes and particle densities. Since a drug with specific characteristics (i.e. particle size and density) exhibits maximum deposition on particular lung areas, the current study provides initial indications to a qualified physician for proper selection of medication. Keywords: Computational fluid dynamics, Fluid particle tracing, Obstructive lung diseases, Aerosol deposition, Human airways |
| 32. | Aris S. Lalos; Andreas A. Vasilakis; Anastasios Dimas; Konstantinos Moustakas Adaptive compression of animated meshes by exploiting orthogonal iterations Journal Article In: The Visual Computer, vol. 33, no. 6, pp. 811-821, 2017, ISSN: 1432-2315. @article{Lalos2017orthogonal,We introduce a novel approach to support fast and efficient lossy compression of arbitrary animation sequences ideally suited for real-time scenarios, such as streaming and content creation applications, where input is not known a priori and is dynamically generated. The presented method exploits temporal coherence by altering the principal component analysis (PCA) procedure from a batch- to an adaptive-basis aiming to simultaneously support three important objectives: fast compression times, reduced memory requirements and high-quality reproduction results. A dynamic compression pipeline is presented that can efficiently approximate the k-largest PCA bases based on the previous iteration (frame block) at a significantly lower complexity than directly computing the singular value decomposition. To avoid errors when a fixed number of basis vectors are used for all frame blocks, a flexible solution that automatically identifies the optimal subspace size for each one is also offered. An extensive experimental study is finally offered, showing that the proposed methods are superior in terms of performance as compared to several direct PCA-based schemes while, at the same time, achieves plausible reconstruction output despite the constraints posed by arbitrarily complex animated scenarios. |
| 31. | A.S Lalos; I. Nikolas; E. Vlachos; K. Moustakas Compressed Sensing for Efficient Encoding of Dense 3D Meshes Using Model-Based Bayesian Learning Journal Article In: IEEE Transactions on Multimedia, vol. vol. 19, no. no.1, pp. 41-53, 2017. @article{Lalos2017b,With the growing demand for easy and reliable generation of 3D models representing real-world or synthetic objects, new schemes for acquisition, storage, and transmission of 3D meshes are required. In principle, 3D meshes consist of vertex positions and vertex connectivity. Vertex position encoders are much more resource demanding than connectivity encoders, stressing the need for novel geometry compression schemes. The design of an accurate and efficient geometry compression system can be achieved by increasing the compression ratio without affecting the visual quality of the object and minimizing the computational complexity. In this paper, we present novel compression/reconstruction schemes that enable aggressive compression ratios, without significantly reducing the visual quality. The encoding is performed by simply executing additions/subtractions. The benefits of the proposed method become more apparent as the density of the meshes increases, while it provides a flexible framework to trade efficiency for reconstruction quality. We derive a novel Bayesian learning algorithm that models the most significant graph Fourier transform coefficients of each submesh, as a multivariate Gaussian distribution.Thenwe evaluate iteratively the distribution parameters using the expectation-maximization approach. To improve the performance of the proposed approach in highly under determined problems, we exploit the local smoothness of the partitioned surfaces. Extensive evaluation studies, carried out using a large collection of different 3D models, show that the proposed schemes, as compared to the state-of-the-art approaches, achieve competitive compression ratios, offering at the same time significantly lower encoding complexity. |
| 30. | K. Moustakas; A. S. Lalos An information-theoretic treatment of passive haptic media Journal Article In: Multimedia Tools and Applications, vol. vol. 76, no. no.5, pp. 6189-6208, 2017. @article{Moustakas2016,Haptic rendering has been long considered as the process of estimating the force that stems from the interaction of a user and an object. Even if this approach follows the principles of natural haptic interaction, it places severe limitations in processing haptic media. This paper presents an information theoretic framework that aims to provide a new view of haptic rendering that can accommodate for open-loop synthetic haptic media, where interaction-based rendering is a special case. As a result, using the proposed informationtheoretic approach, the haptic signal can be precomputed as a force field, stored and then filtered by taking into account device and perceptual capabilities of the receiver in order to lower the required bandwidth of the resulting stream, thus opening new possibilities for the representation and processing of haptic media. |
| 29. | T. Dimas J. Lakoumentas A.S Lalos; K. Moustakas Energy Efficient Monitoring of Metered Dose Inhaler Usage Journal Article In: Journal of Medical Systems, vol. To appear, 2016. @article{Lalos2016b,Life-long chronic inflammatory diseases of the airways, such as asthma and Chronic Obstructive Pulmonary Disease, are very common worldwide, affecting people of all ages, race and gender. One of the most important aspects for the effective management of asthma is medication adherence which is defined as the extent to which patients follow their prescribed action plan and use their inhaler correctly. Wireless telemonitoring of the medication adherence can facilitate early diagnosis and management of these diseases through the use of an accurate and energy efficient mHealth system. Therefore, low complexity audio compression schemes need to be integrated with high accuracy classification approaches for the assessment of adherence of patients that use of pressurized Metered Dose Inhalers (pMDIs). To this end, we propose a novel solution that enables the energy efficient monitoring of metered dose inhaler usage, by exploiting the specific characteristics of the reconstructed audio features at the receiver. Simulation studies, carried out with a large dataset of indoor & outdoor measurements have led to high levels of accuracy (98 %) utilizing only 2 % of the recorded audio samples at the receiver, demonstrating the potential of this method for the development of novel energy efficient inhalers and medical devices in the area of respiratory medicine. |
| 28. | J. Gliatis K. Moustakas D. Stanev; C. Koutsojannis ACL Reconstruction Decision Support Personalized Simulation of the Lachman Test and Custom Activities Journal Article In: Methods of Information in Medicine, vol. 55, pp. 98-105, 2015. @article{Stanev2015c,Objectives: The objective of the proposed approach is to develop a clinical decision support system (DSS) that will help clinicians optimally plan the ACL reconstruction procedure in a patient specific manner. Methods: A full body model is developed in this study with 23 degrees of freedom and 93 muscles. The knee ligaments are modeled as non-linear spring-damper systems and a tibiofemoral contact model was utilized. The parameters of the ligaments were calibrated based on an optimization criterion. Forward dynamics were utilized during simulation for predicting the model’s response to a given set of external forces, posture configuration and physiological parameters. Results: The proposed model is quantified using MRI scans and measurements of the well-known Lachman test, on several patients with a torn ACL. The clinical potential of the proposed framework is demonstrated in the context of flexion-extension, gait and jump actions. The clinician is able to modify and fine tune several parameters such as the number of bundles, insertion position on the tibia or femur and the resting length that correspond to the choices of the surgical procedure and study their effect on the biomechanical behavior of the knee. Conclusion: Computational knee models can be used to predict the effect of surgical decisions and to give insight on how different parameters can affect the stability of the knee. Special focus has to be given in proper calibration and experimental validation. |
| 27. | A. Drosou K. Moustakas S. Papadopoulos; D. Tzovaras BGPGraph: Detecting and Visualizing Internet Routing Anomalies Journal Article In: IET Information Security, vol. 10, no. 3, pp. 125-133, 2015. @article{Moustakas2015b,Border gateway protocol (BGP) is the main protocol used on the Internet today, for the exchange of routing information between different networks. The lack of authentication mechanisms in BGP, render it vulnerable to prefix hijacking attacks, which raise serious security concerns regarding both service availability and data privacy. To address these issues, this study presents BGPGraph, a scheme for detecting and visualising Internet routing anomalies. In particular, BGPGraph introduces a novel BGP anomaly metric that quantifies the degree of anomaly on the BGP activity, and enables the analyst to obtain an overview of the BGP status. The analyst, is afterwards able to focus on significant time windows for further analysis, by using a hierarchical graph visualisation scheme. Furthermore, BGPGraph uses a novel method for the quantification of information visualisation that allows for the evaluation, and optimal selection of parameters, in case of the corresponding visual analytics algorithms. As a result, by utilising the proposed approach, four new BGP anomalies were able to be identified. Experimental demonstration in known BGP events, illustrates the significant analytics potential of the proposed approach in terms of identifying prefix hijacks and performing root cause analysis. |
| 26. | K. Moustakas 6DoF haptic rendering using distance maps over implicit representations Journal Article In: Multimedia Tools and Applications, vol. 75, pp. 4543-4557, 2015. @article{Moustakas2015,This paper presents a haptic rendering scheme based on distance maps over implicit surfaces. Using the successful concept of support planes and mappings, a support plane mapping formulation is used so as to generate a convex representation and efficiently perform collision detection. The proposed scheme enables, under specific assumptions, the analytical reconstruction of the rigid 3D object’s surface, using the equations of the support planes and their respective distance map. As a direct consequence, the problem of calculating the force feedback can be analytically solved using only information about the 3D object’s spatial transformation and position of the haptic interaction point. Moreover, several haptic effects are derived by the proposed mesh-free haptic rendering formulation. Experimental evaluation and computational complexity analysis demonstrates that the proposed approach can reduce significantly the computational cost when compared to existing methods. |
| 25. | A. Drosou; D. Ioannidis; D. Tzovaras; K. Moustakas; M. Petrou Prehension Biometrics for Human Authentication Journal Article In: Pattern Recognition, vol. vol. 48, no. no. 5, pp. 1743-1759, 2015. @article{Drosou2015,This paper presents an extensive study on prehension-based dynamic features and their use for biometric purposes. The term prehension describes the combined movement of reaching, grasping and manipulating objects. The motivation behind the proposed study derives from both previous works related to the human physiology and human motion, as well as from the intuitive assumption that different body types and different characters would produce distinguishable, and thus valuable for biometric verification, activity-related traits. A novel approach for analyzing such movements is presented herein, based on the generation of an activity related manifold, the Activity hyper-Surface. The authentication capacity of the extracted features on the activity hyper-surface is evaluated in terms of their relative entropy and their mutual information within a complete framework targeting user verification. Experimental results on two datasets of 29 real subjects each and a third one of 100 virtual subjects show that the introduced concept constitutes a promising approach in the field of biometric recognition. |
| 24. | K. Moustakas P. Moschonas N. Kaklanis; D. Tzovaras Virtual user models for the elderly and disabled for automatic simulated accessibility and ergonomy evaluation of designs Journal Article In: Universal Access in the Information Society, vol. Volume 12, no. Issue 4, pp. 403-425, 2013. @article{Kaklanis2013,This paper presents a framework for automatic simulated accessibility and ergonomy testing of virtual prototypes of products using virtual user models. The proposed virtual user modeling framework describes virtual humans focusing on the elderly and people with disabilities. Geometric, kinematic, physical, behavioral and cognitive aspects of the user affected by possible disabilities are examined, in order to create virtual user models able to represent people with various functional limitations. Hierarchical task and interaction models are introduced, in order to describe the user’s capabilities at multiple levels of abstraction. The use of alternative ways of a user task’s execution, exploiting different modalities and assistive devices, is supported by the proposed task analysis. Experimental results on the accessibility and ergonomy evaluation of different workplace designs for the use of a telephone and a stapler show how the proposed framework can be put into practice and demonstrate its significant potential. |
| 23. | Anastasios Drosou; Dimitrios Tzovaras; Konstantinos Moustakas; Maria Petrou Systematic Error Analysis for the Enhancement of Biometric Systems Using Soft Biometrics Journal Article In: IEEE Signal Processing Letters, vol. 19, no. no. 12, pp. 833-836, 2012. @article{Drosou2012b,This letter presents a novel probabilistic framework for augmenting the recognition performance of biometric systems with information from continuous soft biometric (SB) traits. In particular, by modelling the systematic error induced by the estimation of the SB traits, a modified efficient recognition probability can be extracted including information related both to the hard and SB traits. The proposed approach is applied without loss of generality in the case of gait recognition, where two state-of-the-art gait recognition systems are considered as hard biometrics and the height and stride length of the individuals are considered as SBs. Experimental validation on two known, large datasets illustrates significant advances in the recognition performance with respect to both identification and authentication rates. |
| 22. | D. Tzovaras G. Stavropoulos K. Moustakas Point-based Similarity Estimation Between 2.5D Visual Hulls and 3D Objects Journal Article In: International Journal on Computer Science and Information Systems, vol. 7, no. 1, pp. 18-31, 2012, ISSN: 1646-3692. @article{Moustakas2012c,This paper presents a novel framework for point-based similarity estimation between 3D objects and 2.5D visual hulls. Initially, the protrusion map is estimated for both the visual hull that is generated by a range image and the 3D model that is followed by the extraction of the salient features that correspond to the highly protruding areas of the objects. Then, based on the concept that for a 3D object and a corresponding query range image, there should be a virtual camera with such intrinsic and extrinsic parameters that would generate an optimum range image, in terms of minimizing an error function that takes into account the visual hull and the salient features of the objects, when compared to other parameter sets or other target 3D models, matching is performed via estimating dissimilarity within the range image and salient feature space. Experimental results illustrate the efficiency of the proposed approach in benchmark datasets. |
| 21. | K. Moustakas D. Ioannidis A. Drosou; D. Tzovaras Spatiotemporal analysis of human activities for biometric authentication Journal Article In: Computer Vision and Image Understanding, vol. 116, no. 3, pp. 411 - 421, 2012, ISSN: 1077-3142, (Special issue on Semantic Understanding of Human Behaviors in Image Sequences). @article{Drosou2012c,This paper presents a novel framework for unobtrusive biometric authentication based on the spatiotemporal analysis of human activities. Initially, the subject’s actions that are recorded by a stereoscopic camera, are detected utilizing motion history images. Then, two novel unobtrusive biometric traits are proposed, namely the static anthropometric profile that accurately encodes the inter-subject variability with respect to human body dimensions, while the activity related trait that is based on dynamic motion trajectories encodes the behavioral inter-subject variability for performing a specific action. Subsequently, score level fusion is performed via support vector machines. Finally, an ergonomics-based quality indicator is introduced for the evaluation of the authentication potential for a specific trial. Experimental validation on data from two different datasets, illustrates the significant biometric authentication potential of the proposed framework in realistic scenarios, whereby the user is unobtrusively observed, while the use of the static anthropometric profile is seen to significantly improve performance with respect to state-of-the-art approaches. |
| 20. | A. Drosou; G. Stavropoulos; D. Ioannidis; K. Moustakas; D. Tzovaras Unobtrusive multi-modal biometric recognition using activity-related signatures Journal Article In: IET Computer Vision, vol. 5, iss. 6, pp. 367-379(12), 2011, ISSN: 1751-9632. @article{drosou2011multimodal,The present study proposes a novel multimodal biometrics framework for identity recognition and verification following the concept of the so called ‘on-the-move’ biometry, which sets as the final objective the non-stop authentication in an unobtrusive manner. Gait, that forms the major modality of the scheme, is complemented by new dynamic biometric signatures extracted from several activities performed by the user. Gait recognition is performed through a robust scheme that is based on geometric descriptors of gait energy images and is able to compensate for undesired gait behaviour like walking direction variations and stops. On the other hand, the biometric signatures, based on the user activities, are extracted by tracking of three points of interest and are seen to provide a powerful auxiliary biometric trait. Finally, score level fusion is performed and the experimental results illustrate that the proposed multimodal biometric scheme provides very promising results in realistic application scenarios. |
| 19. | K. Moustakas D. Tzovaras D. Giakoumis; G. Hassapis Automatic Recognition of Boredom in Video Games using novel Biosignal Moment-based Features Journal Article In: IEEE Transactions on Affective Computing, vol. vol. 2, no. no. 3, pp. 119 - 133, 2011. @article{Giakoumis2011b,This paper presents work conducted toward the biosignals-based automatic recognition of boredom, induced during videogame playing. For this purpose, common biosignal feature extraction methods were exploited and their capability to identify boredom was assessed. Moreover, for the first time, Legendre and Krawtchouk moments, as well as novel moment variations, were extracted as biosignal features and their potential toward automatic affect recognition was examined using the specific application scenario. The present analysis was conducted with ECG and GSR data collected from 19 different subjects, while boredom was naturally induced during the repetitive playing of a 3D video game. Conventional biosignal features as well as moment-based ones were found to be effective for the automatic recognition of boredom by achieving classification accuracies around 85 percent. Then, the joint use of moments and moment variations with conventional features was found to significantly improve classification accuracy by producing a maximum correct classification ratio of 94.17 percent |
| 18. | Konstantinos Moustakas; Dimitrios Tzovaras; Laila Dybkjaer; Niels Bernsen; Oya Aran Using Modality Replacement to Facilitate Communication Between Blind and Hearing Impaired People Journal Article In: IEEE Multimedia, vol. vol. 18, no. no. 2, pp. 26 - 37, 2011. @article{Moustakas2011, |
| 17. | K. Moustakas D. Ioannidis A. Drosou; D. Tzovaras Unobtrusive Behavioural and Activity Related Multimodal Biometrics: The ACTIBIO Authentication Concept Journal Article In: The Scientific World Journal: Special Issue on Biometrics, vol. vol. 11, pp. 520-528, 2011. @article{Drosou2011c,Unobtrusive Authentication Using ACTIvity-Related and Soft BIOmetrics (ACTIBIO) is an EU Specific Targeted Research Project (STREP) where new types of biometrics are combined with state-of-the-art unobtrusive technologies in order to enhance security in a wide spectrum of applications. The project aims to develop a modular, robust, multimodal biometrics security authentication and monitoring system, which uses a biodynamic physiological profile, unique for each individual, and advancements of the state of the art in unobtrusive behavioral and other biometrics, such as face, gait recognition, and seat-based anthropometrics. Several shortcomings of existing biometric recognition systems are addressed within this project, which have helped in improving existing sensors, in developing new algorithms, and in designing applications, towards creating new, unobtrusive, biometric authentication procedures in security-sensitive, Ambient Intelligence environments. This paper presents the concept of the ACTIBIO project and describes its unobtrusive authentication demonstrator in a real scenario by focusing on the vision-based biometric recognition modalities. |
| 16. | G. Stavropoulos; P. Moschonas; K. Moustakas; D. Tzovaras; M. G. Strintzis 3D Model Search and Retrieval from Range Images using Salient Features Journal Article In: IEEE Transactions on Multimedia, vol. vol. 12, no. no.7, pp. 692-704, 2010. @article{Stavropoulos2010b,This paper presents a novel framework for partial matching and retrieval of 3-D models based on a query-by-range-image approach. Initially, salient features are extracted for both the query range image and the 3-D target model. The concept behind the proposed algorithm is that, for a 3-D object and a corresponding query range image, there should be a virtual camera with such intrinsic and extrinsic parameters that would generate an optimum range image, in terms of minimizing an error function that takes into account the salient features of the objects, when compared to other parameter sets or other target 3-D models. In the context of the developed framework, a novel method is also proposed to hierarchically search in the parameter space for the optimum solution. Experimental results illustrate the efficiency of the proposed approach even in the presence of noise or occlusion. |
| 15. | A. Mademlis; K. Kostopoulos; K. Moustakas; D. Tzovaras; M. G. Strintzis A Map Search Framework Based on Attributed Graph Matching Journal Article In: IEEE Multimedia, vol. vol. 17, no. no. 3, pp. 24-33, 2010. @article{Mademlis2010, |
| 14. | D. Tzovaras Κ. Moustakas; G. Stavropoulos Gait recognition using geometric features and soft biometrics Journal Article In: IEEE Signal Processing Letters, vol. vol. 17, no. no. 4, pp. 367-370, 2010. @article{Moustakas2010b,This letter presents a novel framework for gait recognition augmented with soft biometric information. Geometric gait analysis is based on Radon transforms and on gait energy images. User height and stride length information is extracted and utilized in a probabilistic framework for the detection of soft biometric features of substantial discrimination power. Experimental validation illustrates that the proposed approach for integrating soft biometric features in gait recognition advances significantly the identification and authentication performance. |
| 13. | Athanasios Vogiannou; Konstantinos Moustakas; Dimitrios Tzovaras; Michael G. Strintzis Enhancing Bounding Volumes using Support Plane Mappings for Collision Detection Journal Article In: Computer Graphics Forum, vol. 29, no. 5, pp. 1595-1604, 2010. @article{vogiannou2010supportplane,Abstract In this paper we present a new method for improving the performance of the widely used Bounding Volume Hierarchies for collision detection. The major contribution of our work is a culling algorithm that serves as a generalization of the Separating Axis Theorem for non parallel axes, based on the well-known concept of support planes. We also provide a rigorous definition of support plane mappings and implementation details regarding the application of the proposed method to commonly used bounding volumes. The paper describes the theoretical foundation and an overall evaluation of the proposed algorithm. It demonstrates its high culling efficiency and in its application, significant improvement of timing performance with different types of bounding volumes and support plane mappings for rigid body simulations. |
| 12. | K. Moustakas; G. Nikolakis; D. Tzovaras; S. Carbini; O. Bernier; J. E. Viallet 3D content-based search using sketches Journal Article In: Springer International Journal on Personal and Ubiquitous Computing, vol. vol. 13, no. no. 1, pp. 59-67, 2009. @article{Moustakas2009,This paper presents a novel interactive framework for 3D content-based search and retrieval using as query model an object that is dynamically sketched by the user. In particular, two approaches are presented for generating the query model. The first approach uses 2D sketching and symbolic representation of the resulting gestures. The second utilizes non-linear least squares minimization to model the 3D point cloud that is generated by the 3D tracking of the user’s hands, using superquadrics. In the context of the proposed framework, three interfaces were integrated to the sketch-based 3D search system including (a) an unobtrusive interface that utilizes pointing gesture recognition to allow the user manipulate objects in 3D, (b) a haptic–VR interface composed by 3D data gloves and a force feedback device, and (c) a simple air–mouse. These interfaces were tested and comparative results were extracted according to usability and efficiency criteria. |
| 11. | G. Nikolakis K. Moustakas D. Tzovaras; M. G. Strintzis Interactive Mixed Reality White Cane Simulation for the Training of the Blind and the Visually Impaired Journal Article In: Springer International Journal on Personal and Ubiquitous Computing, vol. vol. 13, no. no. 1, pp. 51-58, 2009. @article{Tzovaras2009,This paper presents a mixed reality tool developed for the training of the visually impaired based on haptic and auditory feedback. The proposed approach focuses on the development of a highly interactive and extensible Haptic Mixed Reality training system that allows visually impaired to navigate into real size Virtual Reality environments. The system is based on the use of the CyberGraspTM haptic device. An efficient collision detection algorithm based on superquadrics is also integrated into the system so as to allow real time collision detection in complex environments. A set of evaluation tests is designed in order to identify the importance of haptic, auditory and multimodal feedback and to compare the MR cane against the existing Virtual Reality cane simulation system. |
| 10. | Savvas Argyropoulos; Konstantinos Moustakas; Alexey A. Karpov; Oya Aran; Dimitrios Tzovaras; Thanos Tsakiris; Giovanna Varni; Byungjun Kwon Multimodal User Interface for the Communication of the Disabled Journal Article In: Springer International Journal on Multimodal User Interfaces, vol. vol. 2, no. no. 2, pp. 105-116, 2008. @article{Argyropoulos2008b,In this paper, a novel system is proposed to provide alternative tools and interfaces to blind and deaf-andmute people and enable their communication and interaction with the computer. Several modules are developed to transform signals into other perceivable forms so that the transmitted message is conveyed despite one’s disabilities. The proposed application integrates haptics, audio and visual output, computer vision, sign language analysis and synthesis, speech recognition and synthesis to provide an interactive environment where the blind and deaf-and-mute users can collaborate. All the involved technologies are integrated into a treasure hunting game application that is jointly played by the blind and deaf-and-mute user. The integration of the multimodal interfaces into a game application serves both as an entertainment and a pleasant education tool to the users. |
| 9. | T. Pun; P. Roth; G. Bologna; K. Moustakas; D. Tzovaras Image and video processing for visually handicapped people Journal Article In: Eurasip International Journal on Image and Video Processing, vol. vol. 2007, no. ID 25214, pp. 12, 2007. @article{Pun2007,This paper reviews the state of the art in the field of assistive devices for sight-handicapped people. It concentrates in particular on systems that use image and video processing for converting visual data into an alternate rendering modality that will be appropriate for a blind user. Such alternate modalities can be auditory, haptic, or a combination of both. There is thus the need for modality conversion, from the visual modality to another one; this is where image and video processing plays a crucial role. The possible alternate sensory channels are examined with the purpose of using them to present visual information to totally blind persons. Aids that are either already existing or still under development are then presented, where a distinction is made according to the final output channel. Haptic encoding is the most often used by means of either tactile or combined tactile/kinesthetic encoding of the visual data. Auditory encoding may lead to low-cost devices, but there is need to handle high information loss incurred when transforming visual data to auditory one. Despite a higher technical complexity, audio/haptic encoding has the advantage of making use of all available user’s sensory channels. |
| 8. | K. Kostopoulos; K. Moustakas; D. Tzovaras; G. Nikolakis; C. Thillou; B. Gosselin Haptic Access to Conventional 2D Maps for the Visually Impaired Journal Article In: Springer International Journal on Multimodal User Interfaces, vol. vol. 1, no. no. 2, pp. 13-19, 2007. @article{Kostopoulos2007b,This paper describes a framework of map image analysis and presentation of the semantic information to blind users using alternative modalities (i.e. haptics and audio). The resulting haptic-audio representation of the map is used by the blind for navigation and path planning purposes. The proposed framework utilizes novel algorithms for the segmentation of the map images using morphological filters that are able to provide indexed information on both the street network structure and the positions of the street names in the map. Next, off-the-shelf OCR and TTS algorithms are utilized to convert the visual information of the street names into audio messages. Finally, a grooved-line-map representation of the map network is generated and the blind users are able to investigate it using a haptic device. While navigating, audio messages are displayed providing information about the current position of the user (e.g. street name, crossroad notification e.t.c.). Experimental results illustrate that the proposed system is considered very promising for the blind users and has been reported to be a very fast means of generating maps for the blind when compared to other traditional methods like Braille images. |
| 7. | Dimosthenis Ioannidis; Dimitrios Tzovaras; Ioannis G. Damousis; Savvas Argyropoulos; Konstantinos Moustakas Gait Recognition using Compact Feature Extraction Transforms and Depth Information Journal Article In: IEEE Transactions on Information Forensics and Security, vol. vol. 2, no. no. 3, pp. 623-630, 2007. @article{Ioannidis2007b,This paper proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depthrelated data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF “Gait Challenge” and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme. |
| 6. | D. Tzovaras K. Moustakas; G. Nikolakis Simulating the use of ancient technology works using advanced virtual reality technologies Journal Article In: International Journal of Architectural Computing, Special Issue on Cultural Heritage, vol. vol. 5, no. no. 2, pp. 256-282, 2007. @article{Moustakas2007bb,This paper introduces a novel framework for the modeling and interactive simulation of ancient Greek technology works with the use of advanced virtual reality technologies.A novel algorithm is introduced for the realistic and efficient resolution of collisions that is based on an advanced collision detection approach that can also calculate in real-time the force that should be fed back to the user using a haptic device.Thus, the user is capable of manipulating the scene objects in the environment using haptic devices to simulate the sense of touch and stereoscopic imaging so as to be immersed in the virtual environment. Moreover, the virtual hand that simulates the user’s hand is modeled using superquadrics so as to further increase the speed of the simulation and the fidelity of the force feedback. Extended evaluation of the system has been performed with visitors of the Science Center and Technology Museum of Thessaloniki. |
| 5. | K. Moustakas; G. Nikolakis; K. Kostopoulos; D. Tzovaras; M. G. Strintzis Haptic Rendering of Visual Data for the Visually Impaired Journal Article In: IEEE Multimedia, vol. vol. 14, no. no. 1, pp. 62-72, 2007. @article{Moustakas2007b, |
| 4. | D. Tzovaras K. Moustakas; M. G. Strintzis SQ-Map: Efficient Layered Collision Detection and Haptic Rendering Journal Article In: IEEE Transactions on Visualization and Computer Graphics, vol. vol. 13, no. no. 1, pp. 80-93, 2007. @article{Moustakas2007,This paper presents a novel layered and fast framework for real-time collision detection and haptic interaction in virtual environments based on superquadric virtual object modeling. An efficient algorithm is initially proposed for decomposing the complex objects into subobjects suitable for superquadric modeling, based on visual salience and curvature constraints. The distance between the superquadrics and the mesh is then projected onto the superquadric surface, thus generating a distance map (SQ-Map). Approximate collision detection is then performed by computing the analytical equations and distance maps instead of triangle per triangle intersection tests. Collision response is then calculated directly from the superquadric models and realistic smooth force feedback is obtained using analytical formulae and local smoothing on the distance map. Experimental evaluation demonstrates that SQ-Map reduces significantly the computational cost when compared to accurate collision detection methods and does not require the huge amounts of memory demanded by distance field-based methods. Finally, force feedback is calculated directly from the distance map and the superquadric formulae. |
| 3. | K. Moustakas; D. Tzovaras; S. Carbini; O. Bernier; J. E. Viallet; S. Raidt; M. Mancas; M. Dimiccoli; E. Yagci; S. Balci; E. I. Leon; M. G. Strintzis MASTERPIECE: Physical Interaction and 3D content-based search in VR Applications Journal Article In: IEEE Multimedia, vol. vol. 13, no. no. 3, pp. 92-100, 2006. @article{Moustakas2006,Virtual reality interfaces can immerse users into virtual environments from an impressive array of application fields, including entertainment, education, design, and navigation. However, history teaches us that no matter how rich the content is from these applications, it remains out of reach for users without a physical way to interact with it. Multimodal interfaces give users a way to interact with the virtual environment (VE) using more than one complementary modality. Masterpiece (which is short for Multimodal Authoring Tool with SIMILAR Technologies from European Research utilizing a Physical Interface in an Enhanced Collaborative Environment) is a platform for a multimodal natural interface. We integrated Masterpiece into a new authoring tool for designers and engineers that uses 3D search capabilities to access original database content, supporting natural human–computer interaction. Masterpiece increases the user’s immersion with a physical interface that’s easier to use than a traditional mouse and keyboard. The user can generate and manipulate simple 3D objects with a sketch-based approach that integrates a multimodal gesture–speech interface. They can then assemble their 3D parts into more complex objects. Moreover, the user can access a database’s original 3D content using a 3D search engine.1–3 Using the rough sketch they created, users can search for similar 3D content in the database. |
| 2. | D. Tzovaras K. Moustakas; M. G. Strintzis Stereoscopic Video Generation Based on Efficient Layered Structure and Motion Estimation from a Monoscopic Image Sequence Journal Article In: IEEE Transactions on Circuits and Systems for Video Technology, vol. vol. 15, no. no. 8, pp. 1065 - 1073, 2005. @article{Moustakas2005,This paper presents a novel object-based method for the generation of a stereoscopic image sequence from a monoscopic video, using bidirectional two–dimensional motion estimation for the recovery of rigid motion and structure and a Bayesian framework to handle occlusions. The latter is based on extended Kalman filters and an efficient method for reliably tracking object masks. Experimental results show that the layered object scene representation, combined with the proposed algorithm for reliably tracking object masks throughout the sequence, yields very accurate results. |
| 1. | Gerasimos Arvanitis; Evangelia I. Zacharaki; Libor Váŝa; Konstantinos Moustakas Broad-to-Narrow Registration and Identification of 3D Objects in Partially Scanned and Cluttered Point Clouds Journal Article In: IEEE Transactions on Multimedia, vol. 24, 0000. @article{Arvanitis2021b,The new generation 3D scanner devices have revolutionized the way information from 3D objects is acquired, making the process of scene capturing and digitization straightforward. However, the effectiveness and robustness of conventional algorithms for real scene analysis are usually deteriorated due to challenging conditions, such as noise, low resolution, and bad perceptual quality. In this work, we present a methodology for identifying and registering partially-scanned and noisy 3D objects, lying in arbitrary positions in a 3D scene, with corresponding high-quality models. The methodology is assessed on point cloud scenes with multiple objects with large missing parts. The proposed approach does not require connectivity information and is thus generic and computationally efficient, thereby facilitating computationally demanding applications, like augmented reality. The main contributions of this work are the introduction of a layered joint registration and indexing scheme of cluttered partial point clouds using a novel multi-scale saliency extraction technique to identify distinctive regions, and an enhanced similarity criterion for object-to-model matching. The processing time of the process is also accelerated through 3D scene segmentation. Comparisons of the proposed methodology with other state-of-the-art approaches highlight its superiority under challenging conditions. |
Book chapters
| 10. | A. Tsipouriaris; A. Kogkas; C. Triantafyllou; K. Moustakas; C. Koutsojannis Modeling and simulating a virtual physiological knee for ACL reconstruction planning Book Chapter Forthcoming In: Springer Communications in Computer and Information Science (CCIS), Forthcoming, (accepted for publication). @inbook{tsipoyriarisccis, |
| 9. | Dimitrios Dimou; Konstantinos Moustakas Fast 3D scene segmentation and partial object retrieval using local geometric surface features Book Chapter In: vol. 12060, pp. 79–98, Springer, Berlin, Heidelberg, 2020, (Gavrilova M., Tan C., Sourin A. (eds) Transactions on Computational Science XXXVI). @inbook{dimou2020fast,Robotic vision and in particular 3D understanding has attracted intense research efforts the last few years due to its wide range of applications, such as robot-human interaction, augmented and virtual reality etc, and the introduction of low-cost 3D sensing devices. In this paper we explore one of the most popular problems encountered in 3D perception applications, namely the segmentation of a 3D scene and the retrieval of similar objects from a model database. We use a geometric approach for both the segmentation and the retrieval modules that enables us to develop a fast, low-memory footprint system without the use of large-scale annotated datasets. The system is based on the fast computation of surface normals and the encoding power of local geometric features. Our experiments demonstrate that such a complete 3D understanding framework is possible and advantages over other approaches as well as weaknesses are discussed. |
| 8. | Nikolaos Kaklanis; Konstantinos Moustakas; Dimitrios Tsovaras Haptic Rendering of HTML Components and 2D Maps Included in Web Pages Book Chapter In: Multiple Sensorial Media Advances and Applications: New Developments in MulSeMedia, pp. 116-139, IGI Global, 2012. @inbook{Kaklanis2012b, |
| 7. | Anastasios Drosou; Dimosthenis Ioannidis; Georgios Stavropoulos; Konstantinos Moustakas; Dimitrios Tzovaras Biometric Keys for the Encryption of Multimodal Signatures Book Chapter In: 2011, ISBN: 978-953-307-488-7. @inbook{Drosou2011, |
| 6. | Konstantinos Moustakas; Savvas Argyropoulos; Dimitrios Tzovaras A Joint Modality Recognition Framework: Case Studies in Enhanced Speech Recognition for the Disabled and Multimodal Biometric Authentication Book Chapter In: Multimodal Signal Processing: Methods and Techniques to Build Multimodal Interactive Systems, Academic Press, 2009, ISBN: 0-1237-4825-9. @inbook{moustakas2009joint, |
| 5. | Savvas Argyropoulos; Konstantinos Moustakas Modality Replacement Framework for Applications for the Disabled Book Chapter In: pp. 251-270, 2008. @inbook{Argyropoulos2008, |
| 4. | K. Moustakas; P. Daras; D. Tzovaras; M. G. Strintzis Interactive Simulation of Deformable Objects for Medical Applications Book Chapter In: Metin Akay (Ed.): Wiley Encyclopedia of Biomedical Engineering, vol. 13, pp. 361-368, Athens, Greece, 2006, ISBN: 0-471-24967-X. @inbook{Moustakas2006interactive, |
| 3. | Georgios Nikolakis; Dimitrios Koutsonanos; Konstantinos Moustakas; Petros Daras; Dimitrios Tzovaras; Michael Strintzis Haptic Interaction in Medical Virtual Environments Book Chapter In: 2006, ISBN: 9780471740360. @inbook{Nikolakis2006c, |
| 2. | K. Moustakas; G. Nikolakis; D. Tzovaras; M. G. Strintzis Haptic Access to Virtual Models of Real Data for Training the Visually Impaired Book Chapter In: Applied Technologies in Medicine and Neuroscience, pp. 249-252, Verlag Integrative Psychiatrie, Bonn, Germany, 2005, ISBN: 3-85184-027-5. @inbook{Moustakas2005haptic, |
| 1. | K. Moustakas; G. Nikolakis; D. Tzovaras; M.G. Strintzis Haptic Interface for the Performance of a Remote Echography Examination Book Chapter In: Applied Technologies in Medicine and Neuroscience, Verlag Integrative Psychiatrie, 0000, ISBN: 3-85184-027-5. @inbook{moustakas2005echography, |
Conferences
2024 |
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| 210. | Christoforos Vlachos; Konstantinos Moustakas High–Fidelity Haptic Rendering Through Implicit Neural Force Representation Proceedings Article In: International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, pp. 493–506, Springer 2024. @inproceedings{vlachos2024high,The use of neural networks with periodic nonlinearities has been explored for the implicit representation and reconstruction of continuous-time signals. Building upon a previously published network for representing the Signed Distance Function (SDF) of a mesh surface, the Unit Normal Function (UNF) has been introduced. With both functions represented, a penalty-based haptic rendering method has been developed. The method performs well with very large meshes, outperforming other methods by generating continuous, high-fidelity forces free of discontinuities. It achieves high spatial accuracy by sampling a continuous implicit force function, enhancing the realism of haptic feedback in virtual environments. |
| 209. | Agapi Chrysanthakopoulou; Theofilos Chrysikopoulos; Leandros-Nikolaos Arvanitopoulos; Konstantinos Moustakas Beyond Euclid: An Educational VR Journey into Spherical Geometry Proceedings Article In: 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2024. @inproceedings{beyondEuclid,This paper introduces an innovative educational virtual reality (VR) experience aimed at immersing users in the complexities of spherical geometry. Focused on spherical triangles, navigation methods, and map projections, the VR journey offers an interactive platform for learning about Earth’s unique geometry. Users engage in modules that explore the properties of spherical triangles, challenges in earth navigation, and the intricacies of map projections, providing them with insights into geodesics and their practical applications. An innovative calculator is designed that allows users to use a spaceship’s control system for doing calculations, thus creating a more intuitive and engaging experience. |
| 208. | Agapi Chrysanthakopoulou; Konstantinos Moustakas Real-time shader-based shadow and occlusion rendering in AR Proceedings Article In: 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2024. @inproceedings{arShadows, |
| 207. | Efthymios Koukoulis; Gerasimos Arvanitis; Konstantinos Moustakas Unleashing the Power of Generalized Iterative Closest Point for Swift and Effective Point Cloud Registration Proceedings Article In: 2024 IEEE International Conference on Image Processing (ICIP), pp. 3403–3409, IEEE 2024. @inproceedings{koukoulis2024unleashingb,Point cloud registration is crucial for tasks like Simultaneous Localization and Mapping (SLAM) and 3D Reconstruction and relies on geometric information to achieve improved results. However, the complex cost functions involved in registration can pose challenges for general-purpose optimization algorithms. An efficient algorithm has been developed to solve the Generalized Least Squares problem within the Generalized Iterative Closest Point (GICP) framework, enabling faster and more accurate point cloud registration. The model is validated on a real-world autonomous driving dataset, where it outperforms existing methods in terms of both accuracy and real-time performance. |
| 206. | Alexandros Gkillas; Christos Anagnostopoulos; Nikos Piperigkos; Aris S Lalos A Real-time Explainable-by-design Super-Resolution Model for LiDAR SLAM Proceedings Article In: 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 1–8, IEEE 2024. @inproceedings{gkillas2024real,A novel optimization based deep learning architecture has been developed, utilizing the advantages of the range view domain. The mathematical connection between low and high-resolution images has been exploited by transforming 3D point clouds into 2D range images, towards a new optimization problem that combines learnable and handcrafted regularizers, to fully capture the inherent structure of the LiDAR data, i.e., the low-rank properties due to the strong correlations among neighboring points and the ring-like structure. The model leverages a deep learning architecture based on the Deep Unrolling strategy, offering both explainability and computational efficiency, and is validated on a real-world autonomous driving dataset, where it outperforms existing methods in terms of both accuracy and real-time performance. Operating at 50-100 frames per second (fps), the proposed solution significantly enhances real-time capabilities, a major improvement over current methods that fail to meet the critical requirement for real-time operation in SLAM applications. |
| 205. | Christoforos Vlachos; Konstantinos Moustakas High–Fidelity Haptic Rendering Through Implicit Neural Force Representation Proceedings Article In: International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, pp. 493–506, Springer 2024. @inproceedings{vlachos2024highb,The use of neural networks with periodic nonlinearities has been explored for the implicit representation and reconstruction of continuous-time signals. Building upon a previously published network for representing the Signed Distance Function (SDF) of a mesh surface, the Unit Normal Function (UNF) has been introduced. With both functions represented, a penalty-based haptic rendering method has been developed. The method performs well with very large meshes, outperforming other methods by generating continuous, high-fidelity forces free of discontinuities. It achieves high spatial accuracy by sampling a continuous implicit force function, enhancing the realism of haptic feedback in virtual environments. |
2023 |
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| 204. | Iliana Loi; Konstantinos Moustakas Physics-Informed Neural Networks for Predicting Fatigue During Intermittent Isometric Tasks Proceedings Article In: 28th Congress of the European Society of Biomechanics, Maastricht, 2023. @inproceedings{loi23maastricht, |
| 203. | Konstantinos Risvas; Konstantinos Moustakas Predictive Ergonomic Evaluation of Automotive Digital Workspaces Proceedings Article In: 28th Congress of the European Society of Biomechanics, Maastricht, 2023. @inproceedings{risvas23maastricht, |
| 202. | Konstantinos Ntogkas; Gerasimos Arvanitis; Konstantinos Moustakas Deep 3D Geometric Saliency Estimation from Light Field Images Proceedings Article In: 2023 24th International inproceedings on Digital Signal Processing (DSP), pp. 1–5, IEEE 2023. @inproceedings{ntogkas2023deep, |
| 201. | Vlassis Fotis; Ioannis Romanelis; Sokratis Zouras; Athina Kokonozi; Konstantinos Moustakas Enhanced Scene Interpretation and Perception Through 3D Virtual Thermal Imaging Proceedings Article In: International inproceedings on Human-Computer Interaction, pp. 157–167, Springer 2023. @inproceedings{fotis2023enhanced, |
| 200. | Alexandros Gkillas; Gerasimos Arvanitis; Aris S Lalos; Konstantinos Moustakas Federated Learning for Lidar Super Resolution on Automotive Scenes Proceedings Article In: 2023 24th International inproceedings on Digital Signal Processing (DSP), pp. 1–5, IEEE 2023. @inproceedings{gkillas2023federated, |
| 199. | Vasileios Lagoutaris; Konstantinos Moustakas Motion Prediction Of Traffic Agents With Hybrid Recurrent-Convolutional Neural Networks Proceedings Article In: 24th International inproceedings on Digital Signal Processing (DSP), pp. 1–5, IEEE 2023. @inproceedings{lagoutaris2023motion, |
| 198. | Gerasimos Arvanitis; Nikos Piperigkos; Christos Anagnostopoulos; Aris S Lalos; Konstantinos Moustakas Real time enhancement of operator's ergonomics in physical human-robot collaboration scenarios using a multi-stereo camera system Proceedings Article In: 2023 IEEE International inproceedings on Industrial Technology (ICIT), pp. 1–6, IEEE 2023. @inproceedings{arvanitis2023real, |
2022 |
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| 197. | Vlassis Fotis; Ioannis Romanelis; Sokratis Zouras; Athina Kokonozi; Konstantinos Moustakas 3D Virtual Thermal Imaging for Enhanced Scene Thermal Imaging Interpretation and Perception Proceedings Article In: International inproceedings on Edge Intelligence 2022, Patras, 2022. @inproceedings{fotios22edge, |
| 196. | Agapi Chrysanthakopoulou; Athina Kokonozi; Konstantinos Moustakas Immersive and Interactive VR Experience for Highlighting and Interpreting a Historic Event Proceedings Article In: International inproceedings on Edge Intelligence 2022, 2022. @inproceedings{chrys22edge, |
| 195. | Konstantinos Risvas; Konstantinos Moustakas Anterior Cruciate Ligament Surgical Reconstruction through Finite Element Analysis Proceedings Article In: International inproceedings on Edge Intelligence 2022, Patras, 2022. @inproceedings{risvas22edge, |
| 194. | Ioannis Konstantoulas; Elias Dritsas; Konstantinos Moustakas Sleep quality evaluation in rich information data Proceedings Article In: 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–4, IEEE 2022. @inproceedings{konstantoulas2022sleep, |
| 193. | Elias Dritsas; Sotiris Alexiou; Konstantinos Moustakas Efficient data-driven machine learning models for hypertension risk prediction Proceedings Article In: 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–6, IEEE 2022. @inproceedings{dritsas2022efficient, |
| 192. | Georgios Giarmatzis; Sokratis Zouras; Michail Pavlou; Konstantinos Moustakas OACTIVE: VR-based Gait Retraining to Address Knee Osteoarthritis Proceedings Article In: 27th Congress of the European Society of Biomechanics, Porto, 2022. @inproceedings{giarmatzis22porto, |
| 191. | Iliana Loi; Evangelia Zacharaki; Konstantinos Moustakas Integrating ANN-based Real-Time Joint Force Prediction with Deep Auto-Regressive Goal-Driven Motion Synthesis Proceedings Article In: 27th Congress of the European Society of Biomechanics, Porto, 2022. @inproceedings{loi2022porto, |
| 190. | Konstantinos Risvas; Konstantinos Moustakas Comparison Between Transtibial and Anteromedial Portal ACL Reconstruction Through Finite Element Analysis Proceedings Article In: 27th Congress of the European Society of Biomechanics, Porto, 2022. @inproceedings{risvas22porto, |
| 189. | E. I. Zacharaki; A. Triantafyllidis; R. Carreton; M. Loeck; I. Michalelis; G. Michalakis; G. Chantziaras; S. Segkouli; D. Giakoumis; K. Moustakas; K. Votis; D. Tzovaras Smart Workplaces for Older Adults Proceedings Article In: HCI International 2022, Virtual, 2022. @inproceedings{zacharaki22hci, |
| 188. | Evgenia Moustridi; Konstantinos Risvas; Konstantinos Moustakas Predictive Simulation of Single-Leg Landing Scenarios for ACL Injury Risk Factors Evaluation Proceedings Article In: 27th Congress of the European Society of Biomechanics, 2022. @inproceedings{moustridi22porto, |
| 187. | Agapi Chrysanthakopoulou; Konstantinos Kalatzis; George Michalakis; Isidoros Michalellis; Konstantinos Moustakas ArtScape: Gamified Virtual Reality Art Exploration Proceedings Article In: 2022 IEEE inproceedings on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 902-903, 2022. @inproceedings{chrysanthakopoulou2022artscape, |
| 186. | Mohsin Kamal; Christos Kyrkou; Nikos Piperigkos; Andreas Papandreou; Andreas Kloukiniotis; Jordi Casademont; Natlia Porras Mateu; Daniel Baos Castillo; Rodrigo Diaz Rodriguez; Nicola Gregorio Durante; others A comprehensive solution for securing connected and autonomous vehicles Proceedings Article In: 2022 Design, Automation & Test in Europe inproceedings & Exhibition (DATE), pp. 790–795, IEEE 2022. @inproceedings{kamal2022comprehensive, |
| 185. | Gerasimos Damigos; Nefeli Zerva; Angelos Pavlopoulos; Konstantina Chatzikyrkou; Argyro Koumenti; Konstantinos Moustakas; Constantinos Pantos; Iordanis Mourouzis; Athanasios Lourbopoulos; Evangelia I Zacharaki Automated TTC image-based analysis of mouse brain lesions Proceedings Article In: International Work-inproceedings on Bioinformatics and Biomedical Engineering, pp. 135–142, Springer 2022. @inproceedings{damigos2022automated, |
| 184. | Elias Dritsas; Sotiris Alexiou; Konstantinos Moustakas Cardiovascular Disease Risk Prediction with Supervised Machine Learning Techniques. Proceedings Article In: ICT4AWE, pp. 315–321, 2022. @inproceedings{dritsas2022cardiovascular, |
| 183. | Andreas Kloukiniotis; Andreas Papandreou; Christos Anagnostopoulos; Aris Lalos; Petros Kapsalas; Duong-Van Nguyen; Konstantinos Moustakas CarlaScenes: A synthetic dataset for odometry in autonomous driving Proceedings Article In: Proceedings of the IEEE/CVF inproceedings on Computer Vision and Pattern Recognition, pp. 4520–4528, 2022. @inproceedings{kloukiniotis2022carlascenes, |
| 182. | Gerasimos Arvanitis; Stavros Nousias; Aris S Lalos; Konstantinos Moustakas Coarse-to-fine defect detection of heritage 3D models using a CNN learning approach Proceedings Article In: 2022 IEEE 5th International inproceedings on Industrial Cyber-Physical Systems (ICPS), pp. 1–6, IEEE 2022. @inproceedings{arvanitis2022coarse, |
| 181. | Elias Dritsas; Sotiris Alexiou; Konstantinos Moustakas COPD severity prediction in elderly with ML techniques Proceedings Article In: Proceedings of the 15th International inproceedings on PErvasive Technologies Related to Assistive Environments, pp. 185–189, 2022. @inproceedings{dritsas2022copd, |
| 180. | Nikolaos Tsiftsis; Konstantinos Moustakas; Nikolaos Fakotakis Effects of Depth of Field on Focus Using a Virtual Reality Escape Room Proceedings Article In: International inproceedings on Speech and Computer, pp. 665–675, Springer 2022. @inproceedings{tsiftsis2022effects, |
| 179. | Maria Trigka; Elias Dritsas; Konstantinos Moustakas Joint Power and Contrast Shrinking in RGB Images with Exponential Smoothing Proceedings Article In: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp. 1–5, IEEE 2022. @inproceedings{trigka2022joint, |
| 178. | Elias Dritsas; Sotiris Alexiou; Konstantinos Moustakas Metabolic syndrome risk forecasting on elderly with ML techniques Proceedings Article In: 16th Learning and Intelligent Optimization inproceedings, LION16, pp. 460–466, Springer Milos, Greece, 2022. @inproceedings{dritsas2022metabolic, |
| 177. | Iliana Loi; Angeliki Grammatikaki; Panagiotis Tsinganos; Efe Bozkir; Dimitris Ampeliotis; Konstantinos Moustakas; Enkelejda Kasneci; Athanassios Skodras Proportional Myoelectric Control in a Virtual Reality Environment Proceedings Article In: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pp. 1–5, IEEE 2022. @inproceedings{loi2022proportional, |
| 176. | Efstratios Kontopoulos; Gerasimos Arvanitis; Alessandro Zanella; Panagiotis Mitzias; Evangelia I Zacharaki; Pavlos Kosmides; Nikos Piperigkos; Konstantinos Moustakas; Aris S Lalos Semantic data integration for monitoring operators’ ergonomics in an automotive manufacturing setting Proceedings Article In: European Semantic Web inproceedings, pp. 303–306, Springer 2022. @inproceedings{kontopoulos2022semantic, |
| 175. | Elias Dritsas; Sotiris Alexiou; Ioannis Konstantoulas; Konstantinos Moustakas Short-term Glucose Prediction based on Oral Glucose Tolerance Test Values. Proceedings Article In: HEALTHINF, pp. 249–255, 2022. @inproceedings{dritsas2022short, |
| 174. | Andreas Kloukiniotis; Konstantinos Moustakas Vanishing point detection based on the fusion of lidar and image data Proceedings Article In: 30th Mediterranean inproceedings on Control and Automation (MED), pp. 688–692, IEEE 2022. @inproceedings{kloukiniotis2022vanishing, |
2021 |
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| 173. | D. Tsaopoulos C. Kokkotis G. Giarmatzis; K. Moustakas Dynamics Knee Loading in the ACL deficient knee Proceedings Article In: XXVIII Congress of the international society of biomechanics (ISB), 2021. @inproceedings{giarmatzis21isb, |
| 172. | Konstantinos Risvas; Dimitar Stanev; Konstantinos Moustakas Subject-Specific Modeling and Simulation of Anterior Cruciate Ligament Reconstruction Surgery Proceedings Article In: 26th Congress of the European Society of Biomechanics, Milano, 2021. @inproceedings{risvas21milano, |
| 171. | Georgios Giarmatzis; Konstantinos Moustakas Knee Cartilage Loading at Different Gait Speeds Proceedings Article In: 26th Congress of the European Society of Biomechanics, Milano, 2021. @inproceedings{giarmatzis21milano, |
| 170. | M. Pavlou; K. Kalatzis; A. Chrysanthakopoulou; S. Georgakopoulos D. Laskos; D. Voultsidis; K. Moustakas Remote adversarial VR serious game simulating COVID-19 infection spread and protection protocols Proceedings Article In: 2021 IEEE inproceedings on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 683-684, 2021. @inproceedings{pavlou2021b, |
| 169. | Giorgos Papoulias; Otilia Kocsis; Konstantinos Moustakas A Data Quality Assessment Approach in the XXX Time-series Data Imputation Paradigm. Proceedings Article In: IJCCI, pp. 453–459, 2021. @inproceedings{papoulias2021data, |
| 168. | Stavros Nousias; Erion-Vasilis Pikoulis; Christos Mavrokefalidis; Aris S Lalos; Konstantinos Moustakas Accelerating 3D scene analysis for autonomous driving on embedded AI computing platforms Proceedings Article In: 2021 IFIP/IEEE 29th International inproceedings on Very Large Scale Integration (VLSI-SoC), pp. 1–6, IEEE 2021. @inproceedings{nousias2021accelerating, |
| 167. | Sotiris Alexiou; Elias Dritsas; Otilia Kocsis; Konstantinos Moustakas; Nikos Fakotakis An approach for personalized continuous glucose prediction with regression trees Proceedings Article In: 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media inproceedings (SEEDA-CECNSM), pp. 1–6, IEEE 2021. @inproceedings{alexiou2021approach, |
| 166. | Efstratios Kontopoulos; Panagiotis Mitzias; Konstantinos Avgerinakis; Pavlos Kosmides; Nikos Piperigkos; Christos Anagnostopoulos; Aris S Lalos; Nikolaos Stagakis; Gerasimos Arvanitis; Evangelia I Zacharaki; others An extensible semantic data fusion framework for autonomous vehicles Proceedings Article In: The Fifteenth International inproceedings on Advances in Semantic Processing (SEMAPRO 2021), pp. 5–11, 2021. @inproceedings{kontopoulos2021extensible, |
| 165. | Evangelos Chatzikalymnios; Konstantinos Moustakas Autonomous vision-based landing of UAV’s on unstructured terrains Proceedings Article In: 2021 IEEE International inproceedings on Autonomous Systems (ICAS), pp. 1–5, IEEE 2021. @inproceedings{chatzikalymnios2021autonomous, |
| 164. | Konstantinos Kalatzis; Konstantinos Moustakas Blending Collision Avoidance Animation in Synthetically Generated Locomotion Proceedings Article In: 2021 International inproceedings on Cyberworlds (CW), pp. 113–116, IEEE 2021. @inproceedings{kalatzis2021blending, |
| 163. | Nikolaos Anatoliotakis; Panagiotis Koustoumpardis; Konstantinos Moustakas Cloth mechanical parameter estimation and simulation for optimized robotic manipulation Proceedings Article In: Proceedings of the IEEE/CVF International inproceedings on Computer Vision, pp. 2612–2620, 2021. @inproceedings{anatoliotakis2021cloth, |
| 162. | Leonidas Liakopoulos; Nikolaos Stagakis; Evangelia I Zacharaki; Konstantinos Moustakas CNN-based stress and emotion recognition in ambulatory settings Proceedings Article In: 2021 12th international inproceedings on information, intelligence, systems & applications (IISA), pp. 1–8, IEEE 2021. @inproceedings{liakopoulos2021cnn, |
| 161. | Andreas Papandreou; Andreas Kloukiniotis; Aris Lalos; Konstantinos Moustakas Deep multi-modal data analysis and fusion for robust scene understanding in CAVs Proceedings Article In: 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6, IEEE 2021. @inproceedings{papandreou2021deep, |
| 160. | George Michalakis; Aspasia Triantafyllou; Maria Kounalaki; Nicolaos Kotsarinis; Panagiotis Sakellaropoulos; Konstantinos Moustakas Dr. supER: Intubation and Ventilator Troubleshooting VR Simulation Proceedings Article In: 2021 IEEE inproceedings on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 671–672, IEEE 2021. @inproceedings{michalakis2021dr, |
| 159. | Gerasimos Arvanitis; Aris S Lalos; Konstantinos Moustakas Fast spatio-temporal compression of dynamic 3d meshes Proceedings Article In: 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6, IEEE 2021. @inproceedings{arvanitis2021fast, |
| 158. | Elias Dritsas; Nikos Fazakis; Otilia Kocsis; Nikos Fakotakis; Konstantinos Moustakas Long-term hypertension risk prediction with ML techniques in ELSA database Proceedings Article In: Learning and Intelligent Optimization: 15th International inproceedings, LION 15, Athens, Greece, June 20–25, 2021, Revised Selected Papers 15, pp. 113–120, Springer 2021. @inproceedings{dritsas2021long, |
| 157. | Elias Dritsas; Nikos Fazakis; Otilia Kocsis; Konstantinos Moustakas; Nikos Fakotakis Optimal team pairing of elder office employees with machine learning on synthetic data Proceedings Article In: 2021 12th International inproceedings on Information, Intelligence, Systems & Applications (IISA), pp. 1–4, IEEE 2021. @inproceedings{dritsas2021optimal, |
| 156. | Aggeliki Anastasiou; Evangelia I. Zacharaki; Anastasios Tsakas; Konstantinos Moustakas; Dimitris Alexandropoulos Physical Unclonable Functions Based on Holographic Microstructures on Silver Proceedings Article In: CLEO: Science and Innovations, pp. JTu3A–28, Optica Publishing Group 2021. @inproceedings{anastasiou2021physical, |
| 155. | Ioannis Konstantoulas; Otilia Kocsis; Elias Dritsas; Nikos Fakotakis; Konstantinos Moustakas Sleep Quality Monitoring with Human Assisted Corrections. Proceedings Article In: IJCCI, pp. 435–444, 2021. @inproceedings{konstantoulas2021sleep, |
| 154. | George Psomathianos; Nikitas Sourdakos; Konstantinos Moustakas Smoke Diffusion Simulation and Physically-Based Rendering for VR Proceedings Article In: 2021 International inproceedings on Cyberworlds (CW), pp. 117–120, IEEE 2021. @inproceedings{psomathianos2021smoke, |
| 153. | Samantha Jamson; Konstantinos Risvas; Roi Naveiro; D Insua; Konstantinos Moustakas; Mikolaj Kruszewski; Aleksandra Rodak; Alessandro Barisone Towards Acceptance of Automated Driving Systems Proceedings Article In: Proceedings of the 5th International inproceedings on Computer-Human Interaction Research and Applications, pp. 232–239, SCITEPRESS-Science and Technology Publications 2021. @inproceedings{jamson2021towards, |
2020 |
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| 152. | K. Moustakas D. Laskos Real-Time Upper Body Reconstruction and Streaming for Mixed Reality Applications Proceedings Article In: Cyberworlds 2020, 2020. @inproceedings{Laskos2020, |
| 151. | Nikos Piperigkosa; Andreas Papandreou; Andreas Kloukiniotis; J. Casademont; Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas; C. Vitale; C. Kyrkou; C. Laoudias; T. Theocharides; G. Ellinas; P. S. Khodashenas CARAMEL: Artificial Intelligence based cybersecurity for connected and automated vehicles Proceedings Article In: ITS European Congress 2020, Lisbon, 2020. @inproceedings{Piperigkos2020b, |
| 150. | Pavlos Kosmides; Konstantinos Demestichas; Konstantinos Avgerinakis; Konstantinos Risvas; Konstantinos Moustakas; A. Rodak; M. Kruszewski; M. Pedzierska; E. Trouva; A. Barisone When trust meets autonomous mobility Proceedings Article In: ITS European Congress 2020, Lisbon, 2020. @inproceedings{Kosmides2020, |
| 149. | Andrea Giachetti; Silvia Biasotti; Elia Moscoso Thompson; Luigi Fraccarollo; Quang Nguyen; Hai-Dang Nguyen; Minh-Triet Tran; Gerasimos Arvanitis; Ioannis Romanelis; Vlasis Fotis; Konstantinos Moustakas; Claudio Tortorici; Naoufel Wer-ghi; Stefano Berretti SHREC 2020: River gravel characterization Proceedings Article In: 13th EG EuroWorkshop On 3D Object Retrieval 2020 (3DOR'20), Graz, 2020. @inproceedings{Giachetti2020, |
| 148. | K. Moustakas G. Arvanitis I. Romanelis Fast feature curve extraction for similarity estimation of 3D meshes Proceedings Article In: 13th EG EuroWorkshop On 3D Object Retrieval 2020 (3DOR'20), Graz, September 2020, 2020. @inproceedings{Romanelis2020, |
| 147. | N. Piperigkos; A. S. Lalos; K. Berberidis; C. Laoudias; K. Moustakas 5G enabled cooperative localization of connected and semi-autonomous vehicles via sparse Laplacian processing Proceedings Article In: 22nd International inproceedings on Transparent Optical Networks, Bari, 2020. @inproceedings{Piperigkos2020, |
| 146. | C. Kyrkou; A. Papachristodoulou; A. Kloukiniotis; A. Papandreou; A. S. Lalos; K. Moustakas; T. Theocharides Towards Artificial-Intelligence-Based Cybersecurity for Robustifying Automated Driving System Against Camera Sensor Attacks Proceedings Article In: IEEE ISVLSI 2020, Limassol, Cyprus, 2020. @inproceedings{Kyrkou2020, |
| 145. | Sotiris Alexiou; Nikolaos Fazakis; Otilia Kocsis; Nikolaos Fakotakis; Konstantinos Moustakas Sedentary workers recognition based on machine learning Proceedings Article In: PETRA 2020, Corfu, Greece, 2020. @inproceedings{Alexiou2020, |
| 144. | C. Vitale; C. Laoudias; G. Ellinas; J. Casademont; P. S. Khodashenas; N. Piperigkos; A. Kloukiniotis; A. S. Lalos; K. Moustakas; P. B. Lobato; J. M. Castillo; P. Kapsalas; K. P. Hofmann The Caramel Project: A Secure Architecure for Connected and Autonomous Vehicles Proceedings Article In: 2020 European inproceedings on Networks and Communications (EuCNC), Dubrovnik, Croatia, 2020. @inproceedings{Vitale2020, |
| 143. | Konstantinos Moustakas; Aris S. Lalos; Gerasimos Arvanitis Image-based 3D mesh Denoising Through a Block Matching 3D Convolutional Neural Network Filtering Approach Proceedings Article In: IEEE ICME 2020, London, 2020. @inproceedings{Arvanitis2020c, |
| 142. | Stavros Nousias; Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas Mesh saliency detection using Convolutional Neural Networks Proceedings Article In: IEEE ICME 2020, London, 2020. @inproceedings{Nousias2020d, |
| 141. | M. Pavlou; K. Risvas; E. I. Zacharaki; K. Moustakas Biophysics-based simulation of virtual human model interactions in 3D virtual scenes Proceedings Article In: IEEE VR, XRTraining workshop, Atlanta, 2020. @inproceedings{Pavlou2020, |
| 140. | K. Moustakas D. Chamzas 3D Augmented Reality Tangible User Interface using Comodity Hardware Proceedings Article In: International inproceedings on Computer Graphics Theory and Applications, GRAPP 2020, Malta, 2020. @inproceedings{Chamzas2020, |
| 139. | Ioannis Konstantoulas; Otilia Kocsis; Nikos Fakotakis; Konstantinos Moustakas An approach for continuous sleep quality monitoring integrated in the SmartWork system Proceedings Article In: 2020 IEEE International inproceedings on Bioinformatics and Biomedicine (BIBM), pp. 1968–1971, IEEE 2020. @inproceedings{konstantoulas2020approach, |
| 138. | Pavlos Kosmides; Konstantinos Demestichas; Konstantinos Avgerinakis; Eleni Trouva; Stefano Bianchi; Alessandro Barisone; Konstantinos Risvas; Konstantinos Moustakas; Aleksandra Rodak; Mikołaj Kruszewski; others Bringing trust to autonomous mobility Proceedings Article In: 2020 AEIT International inproceedings of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE), pp. 1–6, IEEE 2020. @inproceedings{kosmides2020bringing, |
| 137. | Georgios Giarmatzis; Evangelia I Zacharaki; Konstantinos Moustakas Neural network based prediction of knee contact forces for different gait speeds Proceedings Article In: 2020 IEEE international inproceedings on bioinformatics and biomedicine (BIBM), pp. 2590–2595, IEEE 2020. @inproceedings{giarmatzis2020neural, |
2019 |
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| 136. | Christos Tselios; Stavros Nousias; Dimitris Bitzas; Dimitrios Amaxilatis; Orestis Akrivopoulos; Aris S. Lalos; Konstantinos Moustakas; Ioannis Chatzigiannakis Enhancing an eco-driving gamification platform through wearable and vehicle sensor data integration Proceedings Article In: European inproceedings on Ambient Intelligence, Ami 2019, Rome, 2019. @inproceedings{Tselios2019, |
| 135. | D. Pettas; S. Nousias; E. I. Zacharaki; K. Moustakas Recognition of Breathing Activity and Medication Adherence using LSTM Neural Networks Proceedings Article In: IEEE International inproceedings on Bioinformatics and Bioengineering, IEEE BIBE 2019, Athens, 2019. @inproceedings{Pettas2019, |
| 134. | Stavros Nousias; Aris S. Lalos; Athanasios Kalogeras; Christos Alexakos; Christos Koulamas; Konstantinos Moustakas Sparse Modeling and Optimization Tools for Energy Efficient and Reliable IoT Proceedings Article In: Societal Automation 2019, 2019. @inproceedings{Nousias2019b, |
| 133. | K. Moustakas E. Zacharaki N. Stagakis Hierarchical Image Inpainting by a Deep Context Encoder Exploiting Structural Similarity and Saliency Criteria Proceedings Article In: 12th inproceedings on Computer Vision Systems (ICVS 2019), Thessaloniki, Greece, 2019. @inproceedings{Stagakis2019, |
| 132. | V. Ntalianis; S. Nousias; A. S. Lalos; M. Birbas; K. Moustakas Assessment of medication adherence in respiratory diseases through deep sparse con-volutional coding Proceedings Article In: 24th IEEE International inproceedings on Emerging Technologies and Factory Automation, Zaragoza, Spain, 2019. @inproceedings{Ntalianis2019, |
| 131. | Konstantinos Moustakas; Aris S. Lalos; Gerasimos Arvanitis Saliency Mapping for Processing 3D Meshes in Industrial Modeling Applications Proceedings Article In: IEEE INDIN 2019, Helsinki, 2019. @inproceedings{Arvanitis2019e, |
| 130. | K. Moustakas S. Nousias G. Papoulias Fluid-Structure Interaction Simulation Framework for Cerebral Aneurism Wall Deformation Proceedings Article In: IISA 2019, Patras, 2019. @inproceedings{Papoulias2019, |
| 129. | Antonios Lalas; Eleftheria Polychronidou; Stavros Nousias; Pavlos Evaggelatos; Rafael Kordonias; Gerasimos Arvanitis; Athina Kokonozi; Paschalis Steiropoulos; Alexis Fourlis; Androula Georgiou; Pantelis Angelidis; Evangelos Karvounis; Konstantinos Moustakas; Vassilis Koutkias; Konstantinos Votis; Dimitrios Tzovaras Take-A-Breath: Smart Platform for Self-Management and Support of Patients with Chronic Respiratory Diseases Proceedings Article In: IISA 2019, Patras, 2019. @inproceedings{Lalas2019, |
| 128. | Evangelos Vlachos; Aris S. Lalos; Gerasimos Arvanitis; Konstantinos Moustakas Energy Efficient Transmission of 3D Meshes over mmWave Massive MIMO Systems Proceedings Article In: IEEE ICME 2019, Shanghai, China, 2019. @inproceedings{Vlachos2019, |
| 127. | S. Nousias; G. Papoulias; O. Kocsis; M. Cabrita; A. S. Lalos; K. Moustakas Coping with missing data in an unobtrusive monitoring system for office workers Proceedings Article In: 2019 International inproceedings on Biomedical Innovations and Applications (BIA), Var-na, Bulgaria, 2019. @inproceedings{Nousias2019, |
| 126. | K. Filip; D. Stanev; K. Moustakas An oculomotor model for kinematics and dynamics simulation Proceedings Article In: XXVII Congress of the International Society of Biomechanics (ISB), Calgary, 2019. @inproceedings{Filip2019b, |
| 125. | D. Stanev; K. Moustakas Exploring musculoskeletal redundancy using null space projection for evaluation of knee reaction loads Proceedings Article In: XXVII Congress of the International Society of Biomechanics (ISB), Calgary, 2019. @inproceedings{Stanev2019e, |
| 124. | D. Stanev; K. Moustakas Modeling and analysis of redundant musculoskeletal systems using null space projection Proceedings Article In: XVII International Symposium on Computer Simulation in Biomechanics (TGCS), Canmore, 2019. @inproceedings{Stanev2019ftgcs, |
| 123. | D. Stanev; A. Kokonozi; K. Filip; F. Nikolopoulos; K. Moustakas; L. Benos; L. Spyrou; D. Tsaopoulos; M. Hantes; G. Halatsis; M. Vlihou; K. Malizos; S. Poulios SafeACL: Ligament reconstruction based on subject-specific musculoskeletal and finite element models Proceedings Article In: The 10th International Conference on Information, Intelligence, Systems and Applications (IISA), 2019. @inproceedings{Stanev2019fiisa, |
| 122. | O. Kocsis; K. Moustakas; N. Fakotakis; et.al. SmartWork: designing a smart age-friendly living and working environment for office workers Proceedings Article In: in Proceedings of the 12th ACM International inproceedings on PErvasive Technologies Related to Assistive Environments (PETRA ’19). Association for Computing Machinery, New York, NY, USA, 2019. @inproceedings{Kocsis2019, |
| 121. | Konstantinos Moustakas; Aris S. Lalos; Gerasimos Arvanitis Scalable Coding of Dynamic 3D Meshes for Low-Latency Streaming Applications Proceedings Article In: International Geometry Summit, Vancouver, 2019. @inproceedings{Arvanitis2019d, |
| 120. | Aggeliki Anastasiou; Evangelia I. Zacharaki; Dimitris Alexandropoulos; Konstantinos Moustakas; Nikolaos A. Vainos Machine learning based technique towards smart laser fabrication of CGH Proceedings Article In: 45th International inproceedings on Micro and Nano Engineering, Rhodes Greece, September 2019, 2019. @inproceedings{Anastasiou2019b, |
| 119. | Dimitris Bitzas; Sokratis Zouras; Agapi Chrysanthakopoulou; Dimitrios Laskos; Konstantinos Kalatzis; Michail Pavlou; Ioanna Balasi; Konstantinos Moustakas VitaZ: Gamified Mixed Reality Multisensorial lnteractions Proceedings Article In: 2019 IEEE inproceedings on Virtual Reality and 3D User Interfaces (VR), 2019. @inproceedings{Bitzas2019,This paper presents multiple Mixed Reality 3D interaction, manipulation and simulation techniques in the context of the 2019 3DUI contest of the IEEE VR inproceedings. The proposed schemes provide smart, seamless transition from the real to the virtual world and demonstrate passive haptics, mid-air haptics, object manipulation and abstract entities (time) manipulation. All techniques are integrated in the context of a mixed reality escape-room or treasure-hunt game, where information from both the real and the virtual world is necessary to solve the puzzle. The paper concludes with a discussion on the extensibility and translational application of the approaches in practical problem solving. |
| 118. | Stavros Nousias; Gerasimos Arvanitis; Aris S Lalos; Konstantinos Moustakas Fast mesh denoising with data driven normal filtering using deep autoencoders Proceedings Article In: 2019 IEEE 17th International inproceedings on Industrial Informatics (INDIN), pp. 260–263, IEEE 2019. @inproceedings{nousias2019fast, |
| 117. | Gerasimos Arvanitis; Aris Lalos; Konstantinos Moustakas Feature-aware and content-wise denoising of 3d static and dynamic meshes using deep autoencoders Proceedings Article In: 2019 IEEE international inproceedings on multimedia and expo (ICME), pp. 97–102, IEEE 2019. @inproceedings{arvanitis2019feature, |
2018 |
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| 116. | G. Arvanitis A. Lalos O. Kocsis; K. Moustakas Multi-model short-term prediction schema for mHealth empowering asthma self-management Proceedings Article In: Brains 2018, Larnaca, Cyprus, 2018. @inproceedings{Kocsis2018, |
| 115. | Stavros Nousias; Christos Tselios; Dimitris Bitzas; Dimitrios Amaxilatis; Javier Montesa; Aris S. Lalos; Konstantinos Moustakas; Ioannis Chatzigiannakis Exploiting gamification to improve eco-driving behaviour: the GamECAR approach Proceedings Article In: Brains 2018, Larnaca, Cyprus, 2018. @inproceedings{Nousias2018brains, |
| 114. | K. Moustakas D. Dimou A Framework for 3D Object Segmentation and Retrieval using Local Geometric Surface Features Proceedings Article In: Cyberworlds 2018, 2018. @inproceedings{Dimou2018, |
| 113. | Konstantinos Moustakas; Aris S. Lalos; Gerasimos Arvanitis; Nikolaos Fakotakis Outliers Removal of Highly Dense and Unorganized Point Clouds Acquired by Laser Scanners in Urban Environments Proceedings Article In: Cyberworlds 2018, Singapore, 2018. @inproceedings{Arvanitis2018dc, |
| 112. | I. Manolas; A. S. Lalos; K. Moustakas Parallel 3D Skeleton Extraction Using Mesh Segmentation Proceedings Article In: 2018 International inproceedings on Cyberworlds (CW), 2018. @inproceedings{Manolas2018, |
| 111. | D. Stanev; K. Moustakas The Effect of Kinematic and Dynamic Redundancy on the Assessment of Joint Reaction Loads Proceedings Article In: Virtual Physiological Human (VPH) 2018, Zaragoza, Spain, 2018. @inproceedings{Stanev2018vph, |
| 110. | Gerasimos Arvanitis; Otilia Kocsis; Aris S. Lalos; Stavros Nousias; Konstantinos Moustakas; Nikos Fakotakis 3-Class Prediction of Asthma Control Status Using a Gaussian Mixture Model Approach Proceedings Article In: 10th Hellenic inproceedings on Artificial Intelligence (SETN2018), Patras, Greece, 2018. @inproceedings{Arvanitis2018setn, |
| 109. | Aris S. Lalos; Gerasimos Arvanitis; Aristotelis Spathis-Papadiotis; Konstantinos Moustakas Feature Aware 3D Mesh Compression using Robust Principal Component Analysis Proceedings Article In: IEEE International inproceedings on Multimedia and Expo (ICME), San Diego, USA, 2018. @inproceedings{Lalos2018c, |
| 108. | Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas; Nikos Fakotakis 3D Mesh In-painting using Matrix Completion via Augmented Lagrange Multiplier Method Proceedings Article In: IEEE Image, Video, and Multidimensional Signal Processing (IVMSP 2018), Zagori, Aristi Village, Greece, 2018. @inproceedings{Arvanitis2018c, |
| 107. | Stavros Nousias; Christos Tselios; Dimitris Bitzas; Olivier Orfila; Samantha Jamson; Pablo Mejuto; Dimitrios Amaxilatis; Orestis Akrivopoulos; Ioannis Chatzigiannakis; Aris S. Lalos; Konstantinos Moustakas Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks Proceedings Article In: IEEE Internaltional inproceedings on Pervasive computing and communications, 2018. @inproceedings{Bitzas2018, |
| 106. | K. Gardelis; A. S. Lalos; K. Moustakas Development of an Eco-Driving Simulation Training System with Natural and Haptic Interaction in Virtual Reality Environments Proceedings Article In: 2nd International inproceedings on Human Computer Interaction Theory and Applications (HUCAPP 2018), 2018. @inproceedings{Gardelis2018, |
| 105. | Aris S. Lalos; Gerasimos Arvanitis; Anastasios Dimas; Konstantinos Moustakas Block Based Spectral Processing of Dense 3D Meshes using Orthogonal Iterations Proceedings Article In: 13th International inproceedings on Computer Graphics Theory and Applications (GRAPP 2018), Funchal, Madeira – Portugal, 2018. @inproceedings{Lalos2018b, |
| 104. | Dimitris Kalampalikis; Konstantinos Moustakas Design of a vision substitution vibrotactile vest for the visually impaired Proceedings Article In: Proceedings of the 10th Hellenic inproceedings on Artificial Intelligence, pp. 1–2, 2018. @inproceedings{kalampalikis2018design, |
| 103. | Aggeliki Anastasiou; Otilia Kocsis; Konstantinos Moustakas Exploring machine learning for monitoring and predicting severe asthma exacerbations Proceedings Article In: Proceedings of the 10th Hellenic inproceedings on Artificial Intelligence, pp. 1–6, 2018. @inproceedings{anastasiou2018exploring, |
| 102. | Stavros Nousias; Christos Tselios; Dimitris Bitzas; Aris S Lalos; Konstantinos Moustakas; Ioannis Chatzigiannakis Uncertainty management for wearable iot wristband sensors using laplacian-based matrix completion Proceedings Article In: IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 1–6, IEEE 2018. @inproceedings{nousias2018uncertainty, |
| 101. | Silvia Biasotti; E Moscoso Thompson; Loic Barthe; Stefano Berretti; Andrea Giachetti; Thibault Lejemble; Nicolas Mellado; Konstantinos Moustakas; Iason Manolas; Dimitrios Dimou; others SHREC'18 track: Recognition of geometric patterns over 3D models Proceedings Article In: Eurographics workshop on 3D object retrieval, 2018. @inproceedings{biasotti2018shrec, |
| 100. | Gerasimos Arvanitis; Aris Spathis-Papadiotis; Aris S. Lalos; Konstantinos Moustakas; Nikos Fakotakis Outliers Removal and Consolidation of DYNAMIC Point Cloud Proceedings Article In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3888-3892, 2018. @inproceedings{arvanitis8451099, |
2017 |
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| 99. | A. S. Lalos; K. Gardelis; A. Spathis-Papadiotis; K. Moustakas Gamification of EcoDriving Behaviours through Intelligent Management of Dynamic Car and Driver Information Proceedings Article In: International inproceedings on Smart Cities and Mobility as a Service, Patras, Greece, 2017. @inproceedings{Lalos2017, |
| 98. | T. Vafeiadis; S. Zikos; G. Stavropoulos; D. Ioannidis; S. Krinidis; D. Tzovaras; K. Moustakas Machine Learning Based Occupancy Detection Via The Use of Smart Meters Proceedings Article In: International inproceedings on Energy Science and Electrical Engineering (ICESEE’17), Budapest, Hungary, 2017. @inproceedings{Vafeiadis2017, |
| 97. | Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas; Nikos Fakotakis Real-Time Removing of Outliers and Noise in 3D Point Clouds Applied in Robotic Applications Proceedings Article In: SPECOM 2017 HATFIELD, HERTFORDSHIRE, UK, 2017. @inproceedings{Arvanitis2017, |
| 96. | Evangelos Vlachos; Aris S. Lalos; Konstantinos Moustakas; Kostas Berberidis Efficient graph-based matrix completion on incomplete animated models Proceedings Article In: IEEE International inproceedings on Multimedia and EXPO 2017, Hong Kong, 2017, (World’s FIRST 10K Best Paper Award – Platinum Award). @inproceedings{Vlachos2017, |
| 95. | G. Stavropoulos; D. Giakoumis; K. Moustakas; D. Tzovaras Automatic action recognition for assistive robots to support MCI patients at home Proceedings Article In: PETRA 2017, Rhodes, Greece, 2017. @inproceedings{Stavropoulos2017, |
| 94. | Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas; Nikos Fakotakis Fast and Effective Dynamic Mesh Completion Proceedings Article In: 26th International inproceedings on Computer Graphics, Visualization and Computer Vision, WSCG 2017, Plzen, 2017. @inproceedings{Arvanitis2017c, |
| 93. | Aris S. Lalos; A. A. Vasilakis; A. Dimas; K. Moustakas Adaptive Compression of Animated Meshes by Exploiting Orthogonal Iterations Proceedings Article In: Computer Graphics International (CGI) 2017, Yokohama, Japan, 2017. @inproceedings{Lalos2017cgi, |
| 92. | Theodore Panagiotopoulos; Gerasimos Arvanitis; Konstantinos Moustakas; Nikos Fakotakis Generation and Authoring of Augmented Reality Terrains Through Real-Time Analysis of Map Images Proceedings Article In: Proceedings of 20th Scandinavian inproceedings, SCIA 2017, pp. 480-491, 2017, ISBN: 978-3-319-59126-1. @inproceedings{inproceedings, |
| 91. | A. A. Vasilakis; K. Vardis; G. Papaioannou; K. Moustakas Variable k-buffer using Importance Maps Proceedings Article In: Eurographics 2017, Lyon, France, 2017. @inproceedings{Vasilakis2017, |
| 90. | Gerasimos Arvanitis; Aris S. Lalos; Konstantinos Moustakas; Nikos Fakotakis Weighted regularized laplacian interpolation for consolidation of highly-incomplete time varying point clouds Proceedings Article In: 2017 3DTV-inproceedings: 3D True Vision v2 - Research and Applications in Future 3D Media, Copenhagen, Denmark, 2017. @inproceedings{Arvanitis2017b, |
| 89. | Otilia Kocsis; Gerasimos Arvanitis; Aris Lalos; Konstantinos Moustakas; Jacob K Sont; Persijn J Honkoop; Kian Fan Chung; Matteo Bonini; Omar S Usmani; Stephen Fowler; others Assessing Machine Learning Algorithms for Self-Management of Asthma Proceedings Article In: 2017 E-Health and Bioengineering inproceedings (EHB), pp. 571–574, IEEE 2017. @inproceedings{kocsis2017assessing, |
2016 |
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| 88. | S Nousias; J Lakoumentas; A Lalos; D Kikidis; K Moustakas; K Votis; D Tzovaras Monitoring asthma medication adherence through content based audio classification Proceedings Article In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-5, 2016, ISSN: null. @inproceedings{Nousias2016, |
| 87. | Dimitar Stanev; Alexander Blenkinsop; Kevin Gurney; Konstantinos Moustakas Neuromusculoskeletal Inertial Filtering of Centrally Generated Beta Oscillations in Parkinson's Disease Proceedings Article In: Virtual Physiological Human (VPH), 2016. @inproceedings{Stanev2016b, |
| 86. | Sebastian S. James; Alexander Blenkinsop; Alexander J. Cope; Sean R. Anderson; Chris Papapavlou; Konstantinos Moustakas; Kevin N. Gurney Integrating brain and biomechanics for the study of Parkinson’s disease Proceedings Article In: Virtual Physiological Human 2016, Amsterdam, 2016. @inproceedings{James2016, |
| 85. | Sebastian S. James; Alexander J. Cope; Alexander Blenkinsop; Chris Papapaulou; Konstantinos Moustakas; Sean R. Anderson; Kevin N. Gurney Using the SpineML toolchain to simulate an integrated brain and biomechanical model of the oculomotor system Proceedings Article In: Frontiers Neuroinformatics 2016, Reading, UK, 2016. @inproceedings{James2016b, |
| 84. | Gerasimos Arvanitis; Konstantinos Moustakas; Nikos Fakotakis Online Biometric Identification with Face Analysis in Web Applications Proceedings Article In: International inproceedings on Speech and Computer (SPECOM 2016), pp. 515-522, 2016, ISBN: 978-3-319-43957-0. @inproceedings{Arvanitis2016, |
| 83. | Nikolaos Dimitriou; Georgios Stavropoulos; Konstantinos Moustakas; Dimitrios Tzovaras Multiple object tracking based on motion segmentation of point trajectories Proceedings Article In: 2016 13th IEEE International inproceedings on Advanced Video and Signal Based Surveillance (AVSS), pp. 200-206, 2016. @inproceedings{Dimitriou2016, |
| 82. | Stavros Nousias; Aris Lalos; Konstantinos Moustakas Computational Modeling for Simulating Obstructive Lung Diseases Based on Geometry Processing Methods Proceedings Article In: Digital Human Modeling: Applications in Health, Safety, Ergonomics and Risk Management: 7th International inproceedings, DHM 2016, pp. 100-109, Toronto, Canada, 2016, ISBN: 978-3-319-40246-8. @inproceedings{Nousias2016b, |
| 81. | Aggeliki Anastasiou; John Lakoumentas; Konstantinos Moustakas A mHealth system for Parkinson's disease evaluation using Smartphone test data Proceedings Article In: IEICE Information and Communication Technology Forum, Patras, Greece, 2016. @inproceedings{Anastasiou2016, |
| 80. | Victor Kyriazakos; Giorgos Nikolakis; Konstantinos Moustakas Natural Interaction with 3D Content on Mobile AR Systems Using Gesture Recognition Proceedings Article In: Augmented Reality, Virtual Reality, and Computer Graphics: Third International inproceedings, AVR 2016, pp. 348-357, Lecce, Italy, 2016, ISBN: 978-3-319-40650-3. @inproceedings{Kyriazakos2016, |
| 79. | Aristotelis Spathis-Papadiotis; Konstantinos Moustakas Simulation of Tsunami Impact upon Coastline Proceedings Article In: International inproceedings on Augmented Reality, Virtual Reality and Computer Graphics, pp. 3-15, 2016, ISBN: 978-3-319-40620-6. @inproceedings{Spathis-Papadiotis2016, |
| 78. | D. Stanev; K. Moustakas; J. Gliatis; C. Koutsojannis ACL reconstruction through patient specific simulation of the Lachman test Proceedings Article In: 17th ESSKA Congress, Barcelona, Spain, 2016. @inproceedings{Stanev2016, |
| 77. | A. Lalas; S. Nousias; D. Kikidis; A. Lalos; K. Moustakas; K. Votis; O. Usmani; D. Tzovaras Numerical Assessment of Airflow and Inhaled Particles Attributes in Obstructed Pulmonary System Proceedings Article In: IEEE International inproceedings on Bioinformatics and Biomedicine (BIBM), 2016. @inproceedings{Lalas2016, |
| 76. | Dimitrios Tzovaras; Stefano Valtolina; Jose Abdelnour-Nocera; Konstantinos Votis; Barbara Rita Barricelli; Konstantinos Moustakas; Dimitrios Kikidis Workshop on Mobile Healthcare for the Self-Management of Chronic Diseases and the Empowerment of Patients Proceedings Article In: Proceedings of the 18th International inproceedings on Human-Computer Interaction with Mobile Devices and Services Adjunct, pp. 1069–1072, Association for Computing Machinery, Florence, Italy, 2016, ISBN: 9781450344135. @inproceedings{10.1145/2957265.2965002, |
| 75. | Stavros Nousias; Aris Lalos; Konstantinos Moustakas; Antonios Lalas; Dimitrios Kikidis; Konstantinos Votis; Dimitrios Tzovaras; Omar Usmani; Fan Chung Computational modeling methods for simulating obstructive human lung diseases Proceedings Article In: European Respiratory Journal 2016, Eur Respiratory Soc, 2016. @inproceedings{nousias2016computational, |
2015 |
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| 74. | Marios Bikos; Yuta Itoh; Gudrun Klinker; Konstantinos Moustakas An Interactive Augmented Reality Chess Game Using Bare-Hand Pinch Gestures Proceedings Article In: 2015 International inproceedings on Cyberworlds (CW), Gotland, Sweden, 2015. @inproceedings{Bikos2015, |
| 73. | Victor Kyriazakos; Konstantinos Moustakas A User-Perspective View for Mobile AR Systems Using Discrete Depth Segmentation Proceedings Article In: 2015 International inproceedings on Cyberworlds (CW), pp. 69-72, 2015. @inproceedings{Kyriazakos2015, |
| 72. | Gerasimos Arvanitis; Konstantinos Moustakas; Nikos Fakotakis Real-Time Context Aware Audio Augmented Reality Proceedings Article In: International inproceedings on Speech and Computer (SPECOM 2015), pp. 333-340, 2015, ISBN: 978-3-319-23131-0. @inproceedings{Arvanitis2015, |
| 71. | Dimitar Stanev; Panagiotis Moschonas; Konstantinos Votis; Dimitrios Tzovaras; Konstantinos Moustakas Simulation and Visual Analysis of Neuromusculoskeletal Models and Data Proceedings Article In: 11th IFIP Inter-national inproceedings on Artificial Intelligence Applications and Innovations (AIAI 2015), pp. 411-420, Bayonne, France, 2015. @inproceedings{Stanev2015b, |
| 70. | Aris Lalos; Konstantinos Moustakas Energy efficient telemonitoring of wheezes Proceedings Article In: 2015 23rd European Signal Processing inproceedings (EUSIPCO), pp. 539-543, IEEE, Nice, France, 2015. @inproceedings{Lalos2015, |
| 69. | I. Nikolas; C. Papapavlou; A. Lalos; K. Moustakas Interactive Visualization and Analysis of Eye Biomechanics Proceedings Article In: 9th International inproceedings on Computer Graphics and Visualization, Las Palmas, 2015. @inproceedings{Nikolas2015, |
| 68. | Dimitar Stanev; Konstantinos Moustakas Proprioceptive modeling of the peripheral nervous system as an extension to the biomechanics musculoskeletal models Proceedings Article In: Greek Society for Biomedical Engineering ELEVIT, 2015. @inproceedings{Stanev2015, |
| 67. | Aris Lalos; Iason Nikolas; Konstantinos Moustakas Sparse coding of dense 3D meshes in mobile cloud applications Proceedings Article In: 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 403-408, 2015. @inproceedings{Lalos2015b, |
| 66. | K. Votis; A. S. Lalos; K. Moustakas; D. Tzovaras Analysis Modeling and Sensing of Both Physiological and Environmental Factors for the Customized and Predictive Self-Management of Asthma Proceedings Article In: 6th Panhellenic inproceedings on Biomedical Technology, Athens, Greece, 2015. @inproceedings{Votis2015, |
2014 |
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| 65. | J. Nikolas; K. Moustakas Space and time partitioning for efficient uncluttered scientific visualization Proceedings Article In: IEEE Scientific Visualization 2014 (SciVis contest), 2014. @inproceedings{Nikolas2014, |
| 64. | C. Papapavlou; K. Moustakas Neuromuscular simulation of saccadic eye movement Proceedings Article In: 6th ELEMBIO inproceedings, Patras, Greece, 2014. @inproceedings{Papapavlou2014, |
| 63. | Dimitar Stanev; Konstantinos Moustakas Virtual Human Behavioural Profile Extraction Using Kinect Based Motion Tracking Proceedings Article In: 2014 International inproceedings on Cyberworlds, CW 2014, 2014. @inproceedings{Stanev2014, |
| 62. | G Stavropoulos; S Krinidis; D Ioannidis; K Moustakas; D Tzovaras A building performance evaluation visualization system Proceedings Article In: 2014 IEEE International inproceedings on Big Data (Big Data), pp. 1077-1085, 2014, ISSN: null. @inproceedings{7004342, |
| 61. | Konstantinos Moustakas Six Degrees of Freedom Implicit Haptic Rendering Proceedings Article In: Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos (Ed.): Artificial Intelligence Applications and Innovations : 10th IFIP WG 12.5 International inproceedings, AIAI 2014, pp. 546-555, Rhodes, Greece, 2014, ISBN: 978-3-662-44654-6. @inproceedings{Moustakas2014b,This paper introduces a six degrees of freedom haptic rendering scheme based on an implicit support plane mapping representation of the object geometries. The proposed scheme enables, under specific assumptions, the analytical reconstruction of the rigid 3D object's surface, using the equations of the support planes and their respective distance map. As a direct consequence, the problem of calculating the force feedback can be analytically solved using only information about the 3D object's spatial transformation and position of the haptic probe. Several haptic effects are derived by the proposed mesh-free haptic rendering formulation. Experimental evaluation and computational complexity analysis demonstrates that the proposed approach can reduce significantly the computational cost when compared to existing methods. |
| 60. | Sebastian S. James; Alexander Blenkinsop; Sean R. Anderson; Chris Papapavlou; Konstantinos Moustakas; Kevin N. Gurney A computational framework for describing the saccadic eye movement system of the Parkinsonian digital patient Proceedings Article In: Virtual Physiological Human inproceedings 2014, Trondheim, Norway, 2014. @inproceedings{James2014, |
| 59. | Konstantinos Moustakas; Konstantinos Votis; Dimitrios Tzovaras; Kevin Gurney; Sean R. Anderson; Peter Brown; Michele Hu; Mauro Da Lio; Elisabeth Chroni NoTremor: Virtual, Physiological and Computational Neuromuscular Models for the Predictive Treatment of Parkinson’s Disease Proceedings Article In: Virtual Physiological Human inproceedings 2014, Trondheim, Norway, 2014. @inproceedings{Moustakas2014, |
| 58. | Andreas Tsipouriaris; Alexandros Kogkas; Christina Triantafyllou; Konstantinos Moustakas; Constantinos Koutsojannis Simulation of ACL reconstruction dynamics for optimal rehabilitation planning Proceedings Article In: REHAB 2014, Oldenburg, Germany, 2014. @inproceedings{Tsipouriaris2014, |
| 57. | Chris Papapavlou; Konstantinos Moustakas Physics-based modelling and animation of saccadic eye movement Proceedings Article In: WSCG 2014, Pilsen, Czech Republic, 2014. @inproceedings{Papapavlou2014b, |
| 56. | K Moustakas Haptic rendering using support plane mappings Proceedings Article In: 2014 International inproceedings on Computer Graphics Theory and Applications (GRAPP), pp. 1-8, 2014, ISSN: null. @inproceedings{7296096, |
2013 |
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| 55. | K Moustakas Haptic media from an information-theoretic perspective Proceedings Article In: 2013 IEEE International Symposium on Haptic Audio Visual Environments and Games (HAVE), pp. 81-86, 2013, ISSN: null. @inproceedings{Moustakas2013b, |
| 54. | Stavros Papadopoulos; Konstantinos Moustakas; Dimitrios Tzovaras BGPViewer: Using Graph representations to explore BGP routing changes Proceedings Article In: 2013 18th International inproceedings on Digital Signal Processing (DSP), pp. 1-6, 2013. @inproceedings{Papadopoulos2013, |
| 53. | K. Moustakas Handling haptics as a stand-alone medium Proceedings Article In: 21st International Con-ference on Computer Graphics, Visualization and Computer Vision 2013, WSCG 2013, Plzen, 2013. @inproceedings{Moustakas2013, |
| 52. | Konstantinos Moustakas Free-Form Implicit Haptic Rendering Proceedings Article In: Proceedings of the 5th Joint Virtual Reality inproceedings, pp. 73–76, Eurographics Association, Paris, France, 2013, ISBN: 9783905674477. @inproceedings{Moustakas2013c, |
2012 |
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| 51. | Nikolaos Kaklanis; Konstantinos Moustakas; Dimitrios Tzovaras A Methodology for Generating Virtual User Models of Elderly and Disabled for the Accessibility Assessment of New Products Proceedings Article In: Computers Helping People with Special Needs: 13th International inproceedings, ICCHP 2012, pp. 295-302, Linz, Austria, 2012. @inproceedings{Kaklanis2012, |
| 50. | Stavros Papadopoulos; Konstantinos Moustakas; Dimitrios Tzovaras Hierarchical Visualization of BGP Routing Changes Using Entropy Measures Proceedings Article In: George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Charless Fowlkes; Sen Wang; Min-Hyung Choi; Stephan Mantler; Jürgen Schulze; Daniel Acevedo; Klaus Mueller; Michael Papka (Ed.): Advances in Visual Computing: 8th International Symposium, ISVC 2012, pp. 696-705, Rethymnon, Crete, Greece, 2012, ISBN: 978-3-642-33191-6. @inproceedings{Papadopoulos2012,This paper presents a novel framework for optimizing the visual analysis of network related information, and in particular of Border Gateway Protocol (BGP) updates, using information theoretic measures of both the underlying data and the visual information. More precisely, a hierarchical visualization scheme is proposed using a graph metaphor that is optimized, with respect to information theoretic metrics of several visual mapping parameters. Experimental demonstration in state-of-the-art BGP events, illustrate the flexibility of the proposed framework and the significant analytics effect of the proposed optimization scheme. |
| 49. | Konstantinos Moustakas; Georgios Stavropoulos; Dimitrios Tzovaras Protrusion Fields for 3D Model Search and Retrieval Based on Range Image Queries Proceedings Article In: International Symposium on Visual Computing, pp. 610-619, 2012. @inproceedings{Moustakas2012protrusion, |
| 48. | Athanasios Tsakiris; Panagiotis Moschonas; Konstantinos Moustakas; Dimitrios Tzovaras An Open Framework for Immersive and Non-immersive Accessibility Simulation for Smart Living Spaces Proceedings Article In: 10th international smart homes and health telematics inproceedings on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management, pp. 291-294, 2012. @inproceedings{Tsakiris2012, |
| 47. | P. Moschonas; N. Kaklanis; A. Tsakiris; K. Moustakas; D. Tzovaras An Open Simulation Framework for Automated and Immersive Accessibility Engineering Proceedings Article In: 6th Cambridge Workshop on Universal Access and Assistive Technology, CWUAAT 2012, 2012. @inproceedings{Moschonas2012, |
2011 |
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| 46. | Nikolaos Kaklanis; Panagiotis Moschonas; Konstantinos Moustakas; Dimitrios Tzovaras A Framework for Automatic Simulated Accessibility Assessment in Virtual Environments Proceedings Article In: Digital Human Modeling: Third International inproceedings, ICDHM 2011, pp. 302-311, Orlando, FL, USA, 2011. @inproceedings{Kaklanis2011b, |
| 45. | A Drosou; K Moustakas; D Ioannidis; D Tzovaras Activity related biometric authentication using Spherical Harmonics Proceedings Article In: CVPR 2011 WORKSHOPS, pp. 25-30, 2011, ISSN: 2160-7516. @inproceedings{Drosou2011cvpr,The present study proposes a novel multimodal biometrics framework for identity recognition and verification following the concept of the so called ‘on-the-move’ biometry, which sets as the final objective the non-stop authentication in an unobtrusive manner. Gait, that forms the major modality of the scheme, is complemented by new dynamic biometric signatures extracted from several activities performed by the user. Gait recognition is performed through a robust scheme that is based on geometric descriptors of gait energy images and is able to compensate for undesired gait behaviour like walking direction variations and stops. On the other hand, the biometric signatures, based on the user activities, are extracted by tracking of three points of interest and are seen to provide a powerful auxiliary biometric trait. Finally, score level fusion is performed and the experimental results illustrate that the proposed multimodal biometric scheme provides very promising results in realistic application scenarios. |
| 44. | Konstantinos Moustakas; Georgios Stavropoulos; Dimitrios Tzovaras Point-based similarity estimation between 2.5D visual hulls and 3D objects Proceedings Article In: International inproceedings on Computer Graphics and Visualization (CGVCVIP 2011), pp. 610-619, Rome, Italy, 2011. @inproceedings{Moustakas2012b, |
| 43. | Athanasios Vogiannou; Konstantinos Moustakas; Dimitrios Tzovaras; Michael Strintzis Non-linear Particle Systems for Scalable Simulation of Deformable Models Proceedings Article In: Computer Vision, Imaging and Computer Graphics. Theory and Applications - International Joint inproceedings, VISIGRAPP 2010, pp. 260-273, Angers, France, 2011. @inproceedings{Vogiannou2011, |
| 42. | N. Kaklanis; K. Moustakas; D. Tzovaras An extension of UsiXML enabling the detailed description of users including elderly and disabled Proceedings Article In: International Workshop on Software Support for User Interface Description Language, Interact 2011, Lisbon, 2011. @inproceedings{Kaklanis2011, |
2010 |
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| 41. | N. Kaklanis; P. Moschonas; K. Moustakas; D. Tzovaras Enforcing accessible design of products and services through simulated accessibility evaluation Proceedings Article In: International inproceedings on ICT for ageing and eInclusion, CONFIDENCE 2010, Jyväskylä, Finland, 2010. @inproceedings{Kaklanis2010, |
| 40. | A. Drosou; K. Moustakas; D. Tzovaras Event-based unobtrusive authentication using multi-view image sequences Proceedings Article In: ACM Multimedia 2010 ARTEMIS workshop, Florence, Italy, 2010. @inproceedings{Drosou2010, |
| 39. | Anastasios Drosou; Konstantinos Moustakas; Dimosthenis Ioannidis; Dimitrios Tzovaras Activity Related Biometrics based on Motion Trajectories. Proceedings Article In: BIOSIG 2010, pp. 127-132, Darmstadt, Germany, 2010. @inproceedings{Drosou2010b, |
| 38. | Vasilios Darlagiannis; Konstantinos Moustakas; Dimitrios Tzovaras On geometric and soft shape content-based search Proceedings Article In: IEEE International inproceedings on Image Processing ICIP2010, pp. 3157-3160, Hong Kong, 2010. @inproceedings{Darlagiannis2010, |
| 37. | A. Vogiannou; K. Moustakas; D. Tzovaras; M. G. Strintzis Enhancing Bounding Volumes using Support Plane Mappings for Collision Detection Proceedings Article In: Eurographics Symposium on Geometry Processing 2010, ( SGP 2010 ), Lyon, France, 2010. @inproceedings{Vogiannou2010, |
| 36. | Nikolaos Kaklanis; Konstantinos Moustakas; Konstantinos Votis; Dimitrios Tzovaras A framework for accessibility testing of virtual environments based on UsiXML Proceedings Article In: ACM SIGCHI Symposium on Engineering Interactive Computing Systems, UsiXML-EICS 2010, Berlin, Germany, 2010. @inproceedings{Kaklanis2010d, |
| 35. | Athanasios Vogiannou; Michael Strintzis; Konstantinos Moustakas; Dimitrios Tzovaras A Practical Approach for Applying Non-linear Dynamics to Particle Systems. Proceedings Article In: GRAPP 2010 - Proceedings of the International inproceedings on Computer Graphics Theory and Applications, pp. 46-53, 2010. @inproceedings{Vogiannou2010grapp,In this paper we present a new method for improving the performance of the widely used Bounding Volume Hierarchies for collision detection. The major contribution of our work is a culling algorithm that serves as a generalization of the Separating Axis Theorem for non parallel axes, based on the well-known concept of support planes. We also provide a rigorous definition of support plane mappings and implementation details regarding the application of the proposed method to commonly used bounding volumes. The paper describes the theoretical foundation and an overall evaluation of the proposed algorithm. It demonstrates its high culling efficiency and in its application, significant improvement of timing performance with different types of bounding volumes and support plane mappings for rigid body simulations. |
| 34. | Konstantinos Moustakas; Dimitrios Tzovaras Virtual Simulation of Cultural Heritage Works Using Haptic Interaction Proceedings Article In: Stasinos Konstantopoulos; Stavros Perantonis; Vangelis Karkaletsis; Constantine D Spyropoulos; George Vouros (Ed.): 6th Hellenic inproceedings on Artificial Intelligence (SETN 2010), pp. 389–394, Athens, Greece, 2010, ISBN: 978-3-642-12842-4. @inproceedings{Moustakas2010,This paper presents a virtual reality framework for the modeling and interactive simulation of cultural heritage works with the use of advanced human computer interaction technologies. A novel algorithm is introduced for realistic real-time haptic rendering that is based on an efficient collision detection scheme. Smart software agents assist the user in manipulating the smart objects in the environment, while haptic devices are utilized to simulate the sense of touch. Moreover, the virtual hand that simulates the user's hand is modeled using analytical implicit surfaces so as to further increase the speed of the simulation and the fidelity of the force feedback. The framework has been tested with several ancient technology works and has been evaluated with visitors of the Science Center and Technology Museum of Thessaloniki. |
| 33. | Nikolaos Kaklanis; Konstantinos Votis; Konstantinos Moustakas; Dimitrios Tzovaras 3D HapticWebBrowser: towards universal web navigation for the visually impaired Proceedings Article In: International Cross-Disciplinary inproceedings on Web Accessibility, pp. 25, Raleigh, NC, USA, 2010. @inproceedings{Kaklanis2010c, |
| 32. | Anastasios Drosou; Konstantinos Moustakas; Dimosthenis Ioannidis; Dimitrios Tzovaras On the Potential of Activity Related Recognition Proceedings Article In: International inproceedings on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 340-348, Angers, France, 2010. @inproceedings{Drosou2010c, |
| 31. | T. Tsakiris; K. Moustakas; D. Tzovaras Improved Accessibility in Maps for Visually Impaired Users Proceedings Article In: European Joint inproceedingss on Theory and Practice of Software, Accessible Mainstream Applications, ETAPS 2010, 2010. @inproceedings{Tsakiris2010, |
| 30. | Dimitrios Giakoumis; Athanasios Vogiannou; Ilkka Kosunen; Konstantinos Moustakas; Dimitrios Tzovaras; George Hassapis Identifying Psychophysiological Correlates of Boredom and Negative Mood Induced during HCI Proceedings Article In: International inproceedings on Biomedical Engineering Systems and Technologies, BIOSTEC 2010, pp. 3-12, 2010. @inproceedings{Giakoumis2010, |
| 29. | Georgios Petkos; Vasilios Darlagiannis; Konstantinos Moustakas; Dimitrios Tzovaras Utilizing Treemaps for Multicriterial Search of 3D Objects Proceedings Article In: Joern Kohlhammer; Daniel Keim (Ed.): EuroVAST 2010: International Symposium on Visual Analytics Science and Technology, The Eurographics Association, 2010, ISBN: 978-3-905673-74-6. @inproceedings{Petkos2010b, |
2009 |
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| 28. | Dimitris Giakoumis; Athanasios Vogiannou; Ilkka Kosunen; Dieter Devlaminck; Minkyu Ahn; Ann é; Marie E Burns; Fatemeh Khademi; Konstantinos Moustakas; Dimitrios Tzovaras Multimodal monitoring of the behavioral and physiological state of the user in interactive VR games Proceedings Article In: Proceedings of the eNTERFACE 2009, Genova, Italy, 2009. @inproceedings{Giakoumis2009b, |
| 27. | Konstantinos Moustakas; Dimitrios Tzovaras; Laila Dybkjær; Niels Bernsen A Modality Replacement Framework for the Communication between Blind and Hearing Impaired People Proceedings Article In: HCI International 2009, pp. 226-235, San Diego, USA, 2009. @inproceedings{Moustakas2009hciinter, |
| 26. | Athanasios Vogiannou; Konstantinos Moustakas; Dimitrios Tzovaras; Michael Strintzis Enhancing Haptic Rendering through Predictive Collision Detection Proceedings Article In: HCI International 2009, pp. 394-402, San Diego, USA, 2009. @inproceedings{Vogiannou2009b, |
| 25. | Nikolaos Kaklanis; Dimitrios Tzovaras; Konstantinos Moustakas Haptic Navigation in the World Wide Web Proceedings Article In: HCI International 2009, pp. 707-715, San Diego, USA, 2009. @inproceedings{Kaklanis2009, |
| 24. | Athanasios Vogiannou; Konstantinos Moustakas; Dimitrios Tzovaras; Michael Strintzis A First Approach to Contact-Based Biometrics for User Authentication Proceedings Article In: 3rd IAPR/IEEE International inproceedings on Biometrics, pp. 838-845, Sassari, Italy, 2009. @inproceedings{Vogiannou2009, |
2008 |
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| 23. | Athanasios Vogiannou; M G Strintzis; Konstantinos Moustakas; Dimitrios Tzovaras DISTANCE MAPS FOR COLLISION DETECTION OF DEFORMABLE MODELS Proceedings Article In: IADIS Computer Graphics and Visualization 2008 (CGV 2008) inproceedings, pp. 216-220, 2008. @inproceedings{Vogiannou2008b, |
| 22. | Georgios Stavropoulos; Konstantinos Moustakas; Dimitrios Tzovaras; M G Strintzis A Novel Approach for Range Image to 3D Model Partial Matching Proceedings Article In: Eurographics Workshop on 3D Object Retrieval, Crete, Greece, 2008. @inproceedings{Stavropoulos2008b, |
2007 |
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| 21. | Savvas Argyropoulos; Konstantinos Moustakas; Alexey A. Karpov; Oya Aran; Dimitrios Tzovaras; Thanos Tsakiris; Giovanna Varni; Byungjun Kwon A Multimodal Framework for the Communication of the Disabled Proceedings Article In: Proceedings of the eNTERFACE 2007, Istanbul, Turkey, 2007. @inproceedings{Argyropoulos2007, |
| 20. | K Kostopoulos; K Moustakas; D Tzovaras; G Nikolakis Haptic Access to Conventional 2D Maps for the Visually Impaired Proceedings Article In: 2007 3DTV inproceedings, pp. 1-4, 2007, ISSN: 2161-203X. @inproceedings{Kostopoulos2007, |
| 19. | Dimosthenis Ioannidis; Dimitrios Tzovaras; Konstantinos Moustakas Gait Identification using the 3D Protrusion Transform Proceedings Article In: IEEE International inproceedings on Image Processing ICIP2007, pp. 349-352, 2007, ISBN: 978-1-4244-1437-6. @inproceedings{Ioannidis2007, |
2006 |
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| 18. | G. Nikolakis; K. Moustakas; D. Tzovaras; T. Harissis Interactive Simulation of Ancient Technology Works Proceedings Article In: 5th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (Eurographics), Nikosia, 2006. @inproceedings{Nikolakis2006, |
| 17. | G. Nikolakis; G. Brochard; K. Moustakas; D. Tzovaras; M. G. Strintzis Navigation Training Tool for the Visually Impaired Proceedings Article In: International inproceedings on Assistive Technologies for Vision and Hearing Impairment , CVHI 2006, Kufstein, 2006. @inproceedings{Nikolakis2006b, |
| 16. | K. Moustakas; G. Nikolakis; D. Tzovaras; S. Carbini; O. Bernier; J.E. Viallet 3D content-based search using sketches Proceedings Article In: International conference on Artificial Intelligence Applications and Innovations, pp. 361-368, Athens, 2006. @inproceedings{moustakas2006sketches, |
| 15. | Dimitrios Tzovaras; Konstantinos Moustakas; Georgios Nikolakis; Michael Strintzis Mixed Reality Cane Simulation Proceedings Article In: pp. 353-360, 2006. @inproceedings{Tzovaras2006, |
| 14. | Konstantinos Moustakas; Georgios Nikolakis; Dimitrios Tzovaras; Benoit Deville; Ioannis Marras; Jakov Pavlek Multimodal tools and interfaces for the intercommunication between visually impaired and “deaf and mute” people Proceedings Article In: eINTERFACE'06-SIMILAR NoE Summer Workshop on Multimodal Interfaces, 2006. @inproceedings{moustakas2006multimodal, |
2005 |
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| 13. | Konstantinos Moustakas; Dimitrios Tzovaras; S é; Olivier Bernier; Jean Emmanuel Viallet; Stephan Raidt; Matei Mancas; Mariella Dimiccoli; Enver Yagci; Serdar Balci; Eloisa Ibanez Leon MASTER-PIECE : A Multimodal ( Gesture + Speech ) Interface for 3 D Model Search and Retrieval Integrated in a Virtual Assembly Application Proceedings Article In: Proceedings of the eNTERFACE 2005, pp. 62-75, 2005. @inproceedings{Moustakas2006enterface, |
| 12. | K. Moustakas; G. Nikolakis; D. Tzovaras; M. G. Strintzis Haptic Interaction with a Virtual Environment Dynamically Created from a Monoscopic Video Proceedings Article In: HCI International, Las Vegas, USA, 2005. @inproceedings{Moustakas2005d, |
| 11. | G. Nikolakis; K. Moustakas; D. Tzovaras; M. G. Strintzis Haptic Representation of Images for the Blind and the Visually Impaired Proceedings Article In: HCI International, Las Vegas, USA, 2005. @inproceedings{Nikolakis2005, |
| 10. | K. Moustakas; D. Koutsonanos; D. Tzovaras; M. G. Strintzis Enhancing Costume designer Creativity Utilizing Haptic Interaction in Cloth Editing Applications Proceedings Article In: HCI International, Las Vegas, USA, 2005. @inproceedings{Moustakas2005e, |
| 9. | K. Moustakas; P. Daras; D. Tzovaras; M. G. Strintzis Classification of 3D Models using Combined Semantical and Geometrical Information Proceedings Article In: Workshop towards Semantic Virtual Environments (SVE2005), pp. 77-85, Villars, Switzerland, 2005. @inproceedings{Moustakas2005c, |
| 8. | Konstantinos Moustakas; Georgios Nikolakis; Dimitrios Koutsonanos; Dimitrios Tzovaras; Michael Strintzis Haptic Feedback Using an Efficient Superquadric Based Collision Detection Method. Proceedings Article In: First Joint Eurohaptics inproceedings and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 649-650, Pisa, Italy, 2005. @inproceedings{Moustakas2005g, |
| 7. | K Moustakas; G Nikolakis; D Tzovaras; M G Strintzis A geometry education haptic VR application based on a new virtual hand representation Proceedings Article In: IEEE VR2005, pp. 249-252, Bonn, Germany, 2005, ISSN: 2375-5334. @inproceedings{Moustakas2005ieeevr, |
2004 |
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| 6. | Dimitrios Koutsonanos; Konstantinos Moustakas; Dimitrios Tzovaras; Michael Strintzis Interactive Cloth Editing and Simulation in Virtual Reality Applications for Theater Professionals. Proceedings Article In: 5th International Symposium on Virtual Reality, Archaeology and Cultural Heritage (Eurographics), pp. 37-46, Brussels, 2004. @inproceedings{Koutsonanos2004, |
| 5. | Konstantinos Moustakas; Dimitrios Tzovaras; Michael Strintzis Optimal hierarchical representation and simulation of cloth and deformable objects Proceedings Article In: IEEE International inproceedings on Image Processing ICIP2004, pp. 3013-3016, Singapore, 2004. @inproceedings{Moustakas2004d, |
| 4. | K Moustakas; D Tzovaras; M G Strintzis A non causal Bayesian framework for object tracking and occlusion handling for the synthesis of stereoscopic video Proceedings Article In: 2nd International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'04), pp. 147-154, Thessaloniki, Greece, 2004, ISSN: null. @inproceedings{Moustakas2004c, |
| 3. | K. Moustakas; D. Tzovaras; M. G. Strintzis Fast Hierarchical Simulation of Cloth and Deformable Objects using an Optimal Pyramidal Representation Proceedings Article In: International inproceedings on Computer Animation and Social Agents, CASA2004, pp. 163-170, Geneva, Switzerland, 2004. @inproceedings{Moustakas2004, |
| 2. | K. Moustakas; G. Nikolakis; D. Tzovaras; M. G. Strintzis Extraction of 3D Scene Structure from a Video for the Generation of 3D Visual and Haptic Representations Proceedings Article In: W3C Workshop on Multimodal Interaction, Sophia Antipolis, France, 2004. @inproceedings{Moustakas2004b, |
2003 |
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| 1. | K. Moustakas; D. Tzovaras; M. G. Strintzis Stereoscopic Video Authoring Tool based on Efficient Object-based Structure and Motion Estimation from a Monoscopic Image Sequence Proceedings Article In: 2nd International Workshop on ICTs, Arts and Cultural Heritage, Athens, Greece, 2003. @inproceedings{Moustakas2003, |