@article {170, title = {A 3D wrist motion-based sign language video summarization technique}, journal = {Pattern Recognition Letters}, volume = {189}, year = {2025}, pages = {23-30}, abstract = {An interesting problem in many video-based applications is the generation of short synopses by selecting the most informative frames, a procedure which is known as video summarization. For sign language videos the benefits of using the t-parameterized counterpart of the curvature of the 2-D signer{\textquoteright}s wrist trajectory to identify keyframes, have been reported in the literature [1]. In this paper we extend these ideas by modeling the 3-D hand motion that is extracted from each frame of the video. To this end we propose a new informative function based on the t-parameterized curvature and torsion of the 3-D trajectory. The method to characterize video frames as keyframes depends on whether the motion occurs in 2-D or 3-D space. Specifically, in the case of 3-D motion we look for the maxima of the harmonic mean of the curvature and torsion of the target{\textquoteright}s trajectory; in the planar motion case we seek for the maxima of the trajectory{\textquoteright}s curvature. The proposed 3-D feature is experimentally evaluated in applications of sign language videos on (1) objective measures using ground-truth keyframe annotations, (2) human-based evaluation of understanding, and (3) in the gloss classification problem. The results obtained are promising.}, keywords = {Frenet{\textendash}Serret frame, Sign language, Torsion, video summarization}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2024.12.015}, url = {https://www.sciencedirect.com/science/article/pii/S0167865524003702}, author = {Evangelos G. Sartinas and Emmanouil Z. Psarakis and Dimitrios I. Kosmopoulos} } @article {162, title = {A generative model for the Mycenaean Linear B script and its application in infilling text from ancient tablets}, journal = {ACM Journal on Computing and Cultural Heritage}, volume = {16}, year = {2023}, pages = {1-25}, abstract = {We present a generative neural language model for the most ancient proven stage of the Greek language, the Mycenaean Greek attributed by the Linear B script. To capture the statistical structure of the Mycenaean documents we present a Bidirectional Recurrent Neural Network and compare it to the traditionally used n-grams. The model is used to supplement the damaged parts of the Mycenaean texts, namely the incomplete, to a greater or lesser extent, words, which are typically discovered on partially damaged clay tablets. We verify our method experimentally using ground - truth, then we demonstrate our results on real cases and compare with experts{\textquoteright} opinions. We also present a methodology to augment our dataset, which turns out to improve our results.}, author = {K. PAPAVASSILEIOU and Dimitrios I. Kosmopoulos} } @article {132, title = {Facilitation of Air Traffic Control via OCR-based Aircraft Registration Number Extraction}, journal = {IET Intelligent Transportation Systems}, volume = {12}, year = {2018}, pages = {965-975}, abstract = {

To identify any aircraft in the world, it is sufficient to read its registration number. This number is a unique identifier, and offers valuable information, in the same way a car registration number does. In this work we present the results of our feasibility study towards a simple, yet very efficient and effective system to identify aircrafts using video-Optical Character Recognition acquired by off-the-shelf cameras. We used several videos under realistic conditions at the Heraklion airport during high-season and we achieved very promising results. We claim that there is much room for the development of a low-cost airport surface monitoring system based on standard cameras, which can complement high-cost radars.

}, author = {Dimitrios G. Vidakis and Dimitrios I. Kosmopoulos} } @proceedings {130, title = {A Prototype Towards Modeling Visual Data Using Decentralized Generative Adversarial Networks}, journal = {IEEE International Conference on Image Processing}, year = {2018}, month = {in press}, abstract = {

Decentralized computation is crucial for training on large data sets, which are stored in various locations. This paper proposes a method for collaboratively training generative adversarial networks (GANs) on several nodes to approach this problem. Each node has access to its local private dataset and model, but can influence and can be influenced by neighboring nodes. Such an approach avoids the need of a fusion center, offers better network load balance, higher robustness to network errors and improves data privacy. This work proposes an initial approach for decentralized GAN network optimization which is based on discriminator exchange among neighbors using an information gain criterion. We present our initial experiments to verify whether such a decentralized architecture can provide useful results.

}, author = {Dimitrios I. 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 {128, title = {A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis}, journal = {International Conference on Computer Vision}, year = {2015}, abstract = {
Unsupervised feature learning algorithms based on con- volutional formulations of independent components analysis (ICA) have been demonstrated to yield state-of-the-art results in several action recognition benchmarks. However, existing approaches do not allow for the number of latent components (features) to be automatically inferred from the data in an unsupervised manner. This is a significant dis- advantage of the state-of-the-art, as it results in consider- able burden imposed on researchers and practitioners, who must resort to tedious cross-validation procedures to obtain the optimal number of latent features. To resolve these issues, in this paper we introduce a convolutional nonpara- metric Bayesian sparse ICA architecture for overcomplete feature learning from high-dimensional data. Our method utilizes an Indian buffet process prior to facilitate inference of the appropriate number of latent features under a hybrid variational inference algorithm, scalable to massive datasets. As we show, our model can be naturally used to obtain deep unsupervised hierarchical feature extractors, by greedily stacking successive model layers, similar to existing approaches. In addition, inference for this model is completely heuristics-free; thus, it obviates the need of te- dious parameter tuning, which is a major challenge most deep learning approaches are faced with. We evaluate our method on several action recognition benchmarks, and ex- hibit its advantages over the state-of-the-art.
}, author = {Sotirios P. Chatzis and Dimitrios I. Kosmopoulos} } @proceedings {126, title = {A Non-Stationary Infinite Partially Observable Markov Decision Process}, journal = {International Conference on Artificial Neural Networks}, year = {2014}, abstract = {Partially Observable Markov Decision Processes (POMDPs) have been met with great success in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Recently, nonparametric Bayesian methods have been successfully applied to POMDPs to obviate the need of a priori knowledge of the size of the state space, allowing to assume that the number of visited states may grow as the agent explores its environment. These approaches rely on the assumption that the agent{\textquoteright}s environment remains stationary; however, in real-world scenarios the environment may change over time. In this work, we aim to address this inadequacy by introducing a dynamic nonparametric Bayesian POMDP model that both allows for automatic inference of the (distributional) representations of POMDP states, and for capturing non-stationarity in the modeled environments. Formulation of our method is based on imposition of a suitable dynamic hierarchical Dirichlet process (dHDP) prior over state transitions. We derive efficient algorithms for model inference and action planning and evaluate it on several benchmark tasks.}, author = {Sotirios P. Chatzis and Dimitrios I. Kosmopoulos} } @article {123, title = {A top-down event-driven approach for concurrent activity recognition}, journal = {Multimedia Tools Appl.}, volume = {69}, year = {2014}, pages = {293-311}, abstract = {

In this paper a framework for automatic online workflow recognition in industrial environments where the issue of concurrent activities rises, is presented. The framework consists of three main parts: The first part is devoted to detecting activity in specific Regions of Interest (ROIs) of the video sequence. This is effected by separating each frame into ROIs and representing the resulting subimages through feature vectors. By observing these vectors we can determine when there is action in a particular ROI. The second part of the framework lies in examining whether the detected activity corresponds to a workflow related event. This is accomplished by HMM modeling. Finally, the third part employs a string matching based technique to confirm the validity of the observed sequence of events or correct any detection or classification errors. This last step also addresses a top down approach by informing lower system levels (such as image representation or object tracking) about the errors committed. The performance of the proposed approach is thoroughly evaluated under real-life complex visual workflow understanding scenarios, in an industrial plant. The obtained results are compared and discussed.

}, keywords = {Concurrent activity recognition Event detection Top-down Region of interest (ROI) Workflow}, author = {Athanasios Voulodimos and Dimitrios I. Kosmopoulos and Nikolaos D. Doulamis and Theodora A. Varvarigou} } @conference {101, title = {Audio-Visual Fusion for Detecting Violent Scenes in Videos}, booktitle = {Artificial Intelligence: Theories, Models and Applications, 6th Hellenic Conference on AI, SETN 2010, Athens, Greece, May 4-7, 2010. Proceedings}, year = {2010}, publisher = {Springer}, organization = {Springer}, author = {Theodoros Giannakopoulos and Alexandros Makris and Dimitrios I. Kosmopoulos and Stavros J. Perantonis and Sergios Theodoridis} } @proceedings {73, title = {Robust Occlusion Handling with Multiple Cameras using a Homography Constraint}, journal = {Fourth International Conference on Computer Vision Theory and Applications}, volume = {2}, year = {2009}, pages = {560-565}, publisher = {INSTICC Press}, address = {Lisboa, Portugal}, author = {Anastasios L. Kesidis and Dimitrios I. Kosmopoulos} }