@conference {167, title = {Procrustes-DTW: Dynamic Time Warping Variant for the Recognition of Sign Language Utterances}, booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}, year = {2023}, abstract = {[color=$\#$333333; font-family: {\textquoteright}HelveticaNeue Regular{\textquoteright}, sans-serif; font-size: 18px; font-variant-ligatures: normal; orphans: 2; widows: 2; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial]In this paper we present a method for classifying sign language videos using a variation of dynamic time warping with the Procrustes distance, which is a shape similarity measure appropriate for handshape recognition. We initially extract features from the dominant hand. We then classify the signs using a nearest-neighbor scheme which includes a dynamic time warping variant, which we dub Procrustes-DTW and which is suitable for extracting a similarity measure of sequences of handshapes only. To reduce computational costs it is combined with a curvature-based summarization scheme with a reasonable sacrifice in accuracy. We verify experimentally our approach on a custom dataset and we show its merit compared to the conventional dynamic time warping approach.[/color]}, doi = {10.1109/ICASSPW59220.2023.10193012}, author = {Arvanitis, Nikolaos and Sartinas, Evangelos and Kosmopoulos, Dimitrios} } @proceedings {153, title = {A 2-D Wrist Motion Based Sign Language Video Summarization}, journal = {British Machine Vision Conference - ORAL PRESENTATION}, year = {2021}, abstract = {In this paper we present a keyframe extraction scheme based on the wrist motion using differential geometry. More specifically, the time (t)-parameterized Frennet-Serret frame for tracking the signer{\textquoteright}s wrist is used and the curvature of the trajectory, is proposed for the identification of the Sign Language (SL) video keyframes. Specifically, a video frame is characterized as keyframe if on that time instance the t-parameterized curvature function attains a maximum value. Finally, in order to properly define the wrist 2-D motion model, a skeleton tracker is used. The proposed scheme is adaptable, i.e., the number of extracted keyframes varies according to the complexity of the signs, while preserving the semantic content. This in turn makes it attractive for applications like video-calling. Its performance in terms of the achieved compression and intelligibility ratios was evaluated on a ground-truth sequence and outperformed its s-parameterized counterpart (s is the arc length); it also outperformed a moment-based SL summarization technique. Furthermore, the proposed scheme was experimentally evaluated on a dataset containing 5500 signs by SL specialists with very promising results. Finally, the proposed keyframe extraction was evaluated against the aforementioned techniques on the same dataset via the use of a GRU neural network on the gloss classification problem; its superior accuracy in identifying the gloss meaning was confirmed.}, author = {Evangelos Sartinas and Emmanouil Psarakis and Klimis Antzakas and Dimitrios Kosmopoulos} } @article {131, title = {A Survey on Developing Personalized Content Services in Museums}, journal = {Pervasive and Mobile Computing}, volume = {47}, year = {2018}, pages = {54-77}, abstract = {

The personalized content services in museums are motivated by the need to enhance the visitors{\textquoteright} experience through recommendations which consider the context of their visit, and by the need of curators to measure objectively the exhibition{\textquoteright}s impact. We survey the latest advancements in the fields of indoor localization, visitor

profiling, content storage and presentation, as well as curator visualization tools, which are the main elements of such systems, and we highlight their strengths and weaknesses. We present an information architecture, whichmay offer useful insights to researchers and developers. Finally, we present the current challenges and the future

trends.

}, author = {Dimitrios I. Kosmopoulos and George Styliaras} } @proceedings {90, title = {Developing visual competencies for socially assistive robots: the HOBBIT approach}, journal = {6th International Conference on Pervasive Technologies for Assistive Environments}, year = {2013}, abstract = {In this paper, we present our approach towards developing visual competencies for socially assistive robots within the framework of the HOBBIT project. We show how we integrated several vision modules using a layered architectural scheme. Our goal is to endow the mobile robot with visual perception capabilities so that it can interact with the users. We present the key modules of independent motion detection, object detection, body localization, person tracking, head pose estimation and action recognition and we explain how they serve the goal of natural integration of robots in social environments.}, author = {Kostas Papoutsakis and Pashalis Panteleris and Antonis Ntelidakis and Stavros Stefanou and Xenofon Zabulis and Dimitrios Kosmopoulos and Antonis Argyros} } @conference {86, title = {Hand Shape and 3D Pose Estimation Using Depth Data from a Single Cluttered Frame.}, booktitle = {International Symposium on Visual Computing (ISVC)}, year = {2012}, publisher = {Springer}, organization = {Springer}, keywords = {dblp}, isbn = {978-3-642-33178-7}, author = {Doliotis, Paul and Athitsos, Vassilis and Kosmopoulos, Dimitrios I. and Perantonis, Stavros J.}, editor = {Bebis, George and Boyle, Richard and Parvin, Bahram and Koracin, Darko and Fowlkes, Charless and Wang, Sen and Choi, Min-Hyung and Mantler, Stephan and Schulze, J{\"u}rgen P. and Acevedo, Daniel and Mueller, Klaus and Papka, Michael E.} } @article {21, title = {A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments}, journal = {MultiMedia, IEEE}, volume = {19}, year = {2012}, month = {July-Sept.}, pages = {42 -52}, issn = {1070-986X}, doi = {10.1109/MMUL.2012.31}, author = {Athanasios Voulodimos and D. Kosmopoulos and Vasileiou, Georgios and Sardis, Emmanuel and Anagnostopoulos, Vasileios and Lalos, Constantinos and Doulamis, A.D. and Varvarigou, T.A.} } @proceedings {10, title = {A Dataset for Workflow Recognition in Industrial Scenes}, journal = {IEEE International Conference on Image Processing}, year = {2011}, pages = {3310-3313}, author = {A. Voulodimos and D. Kosmopoulos and Vasileiou, G. and Sardis, E. and Doulamis, A.D. and Anagnostopoulos, V. and Lalos, C. and Varvarigou, T.A.} }