@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} } @conference {180, title = {Cross-Domain Recommendations Using Attention and~Multitask Learning}, booktitle = {Artificial Intelligence Applications and Innovations}, year = {2025}, publisher = {Springer Nature Switzerland}, organization = {Springer Nature Switzerland}, address = {Limassol}, abstract = {In this study, we propose a novel deep learning method for cross-domain recommendations that effectively combines attention mechanisms, autoencoders, and multitask learning. Our approach leverages multiple datasets from diverse domains and incorporates domain-specific encoders, a shared self-attention mechanism, and a multilayer perceptron (MLP) to capture both intra-domain and inter-domain relationships. By jointly modeling these interactions, we improve recommendation accuracy across domains. Experimental results using the MovieLens dataset demonstrate that our proposed cross-domain recommendation system outperforms traditional approaches including matrix factorization, standard MLPs, and self-attention-based baselines.}, isbn = {978-3-031-96228-8}, doi = {https://doi.org/10.1007/978-3-031-96228-8_29}, author = {Papachronopoulos, Gerasimos and Gkillas, Alexandros and Kosmopoulos, Dimitrios}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros and Andreou, Andreas and Papaleonidas, Antonios} } @conference {179, title = {Geometric Knowledge Distillation via Procrustes Analysis for Efficient Motion Sequence Classification}, booktitle = {2025 25th International Conference on Digital Signal Processing (DSP)}, year = {2025}, month = {June 25{\textendash}27}, publisher = {IEEE}, organization = {IEEE}, address = {Costa Navarino, Messinia, Greece}, abstract = {Motion sequence classification methods that rely on geometric approaches{\textemdash}such as Procrustes analysis and Dynamic Time Warping (DTW){\textemdash}offer high accuracy but are often unsuitable for real-time applications due to their computational cost. In this paper, we present a novel geometric knowledge distillation framework that bridges the gap between accuracy and efficiency by transferring rich geometric insights from a Procrustes-DTW-based distance metric into a transformer-based neural network. By generating soft probability distributions from pre-computed Procrustes-DTW distances, we effectively guide the student model{\textquoteright}s training to preserve essential geometric properties like shape similarity, temporal alignment, and spatial transformation invariance. Our method enables fast and scalable motion sequence classification while retaining the benefits of geometric interpretability. We evaluate our framework on two benchmark tasks: sign language recognition using the SIGNUM dataset and human action recognition on UTD-MHAD. Results show that our distillation approach significantly improves classification accuracy over standard supervised learning and achieves dramatically lower inference time compared to traditional geometric methods{\textemdash}making it ideal for real-time motion understanding in wearable, robotic, and interactive systems.}, keywords = {Action Recognition, Efficient Inference, Geometric Distance Learning, Knowledge Distillation, Procrustes Analysis, Sign Language Recognition, Skeletal Sequences}, author = {De, Bikram and Blekos, Kostas and Pikoulis, Vasilis and Kosmopoulos, Dimitrios and Metsis, Vangelis} } @article {183, title = {Towards more Inclusive Museums Using AI: Guidance in Sign Language}, journal = {ERCIM News}, volume = {141}, year = {2025}, month = {April}, pages = {21{\textendash}22}, author = {Kosmopoulos, Dimitrios and Papadopoulou, Evanthia and Argyros, Antonis} } @conference {181, title = {Automated classification of olive fruit for enhanced olive oil production using computer vision}, booktitle = {Optics, Photonics, and Digital Technologies for Imaging Applications VIII}, year = {2024}, publisher = {SPIE}, organization = {SPIE}, address = {Strasbourg, France}, doi = {10.1117/12.3023019}, author = {Pikoulis, Erion-Vasilis and Blekos, Kostas and Kosmopoulos, Dimitrios} } @proceedings {169, title = {Restoring Mycenaean Linear B {\textquoteright}A \& B{\textquoteright} series tablets using supervised and transfer learning}, journal = {Association for Computational Linguistics Conference - Workshop on Machine Learning for Ancient Languages}, volume = {(accepted)}, year = {2024}, abstract = {We investigate the problem of restoring Mycenaean linear B clay tablets, dating from about 1400 B.C. to roughly 1200 B.C., by using text infilling methods based on machine learning models. Our goals here are: first to try to improve the results of the methods used in the related literature by focusing on the characteristics of the Mycenaean Linear B writing system (series D), second to examine the same problem for the first time on series A\&B and finally to investigate transfer learning using series D as source and the smaller series A\&B as target. Our results show promising results in the supervised learning tasks, while further investigation is needed to better exploit the merits of transfer learning.}, author = {K PAPAVASSILEIOU and Dimitrios Kosmopoulos} } @proceedings {168, title = {Developing Robust and Lightweight Adversarial Defenders by Enforcing Orthogonality on Attack-Agnostic Denoising Autoencoders}, journal = {International Conference on Computer Vision, Workshop on Resource Efficient Deep Learning for Computer Vision}, year = {2023}, abstract = {Adversarial attacks have become a critical threat to the security and reliability of machine learning models. We propose a solution to the problem of defending against adversarial attacks using a deep Denoising Auto Encoder (DAE). The proposed DAE is trained to enforce orthogonality between the noise and the range space of its output in each layer of the encoder{\textquoteright}s chain. Furthermore, the pseudoinverse decoder of the DAE is designed to ensure that the reconstructed image and the null space of its intermediate representations in each layer of the chain maintain orthogonality as it progresses from the target space to the latent space. The denoising problem is formulated as an equality constrained optimization problem, which is solved by finding the stationary points of the Lagrangian function. The noisy data are generated by adding realizations of multiple random noise distributions to pristine data during DAE training, resulting in excellent denoising performance. We compare the performance of our full weights and tied-weights DAEs, showing that the latter not only has half the complexity of the former, but also outperforms the former in denoising and in strong adversarial attacks. To demonstrate the effectiveness of the proposed solution we evaluate our networks against recent works in the literature, specifically those focusing on defending against adversarial attacks.}, author = {Aristeidis Bifis and Emmanuel Psarakis and Dimitrios 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 {156, title = {Context-Aware Automatic Sign Language Video Transcription in Psychiatric Interviews}, journal = {Sensors}, volume = {22}, year = {2022}, pages = {2656}, abstract = {Sign language (SL) translation constitutes an extremely challenging task when undertaken in a general unconstrained setup, especially in the absence of vast training datasets that enable the use of end-to-end solutions employing deep architectures. In such cases, the ability to incorporate prior information can yield a significant improvement in the translation results by greatly restricting the search space of the potential solutions. In this work, we treat the translation problem in the limited confines of psychiatric interviews involving doctor-patient diagnostic sessions for deaf and hard of hearing patients with mental health problems.To overcome the lack of extensive training data and be able to improve the obtained translation performance, we follow a domain-specific approach combining data-driven feature extraction with the incorporation of prior information drawn from the available domain knowledge. This knowledge enables us to model the context of the interviews by using an appropriately defined hierarchical ontology for the contained dialogue, allowing for the classification of the current state of the interview, based on the doctor{\textquoteright}s question. Utilizing this information, video transcription is treated as a sentence retrieval problem. The goal is predicting the patient{\textquoteright}s sentence that has been signed in the SL video based on the available pool of possible responses, given the context of the current exchange. Our experimental evaluation using simulated scenarios of psychiatric interviews demonstrate the significant gains of incorporating context awareness in the system{\textquoteright}s decisions.}, author = {Erion-Vasilis Pikoulis and Aristeidis Bifis and Maria Trigka and Constantinos Constantinopoulos and Dimitrios Kosmopoulos} } @article {164, title = {A Multispectral Dataset for the Detection of Tuta absoluta and Leveillula taurica in Tomato Plants}, journal = {Smart Agricultural Technology}, year = {2022}, pages = {100146}, abstract = {Tomato (Solanum lycopersicum) is one of the most important vegetables for human nutrition and its cultivation employs amounts of resources worldwide. However, tomato cultivation is plagued by several diseases and pests that increase production cost and introduce additional environmental and health risks due to pesticide use. Timely disease and pest detection is of high importance for tomato crop output and the environment, since plant protection input can be optimized. Here, we present a dataset of multispectral images (RGB and NIR) of tomato plants, at various stages of infection with Tuta absoluta and Leveillula taurica, which to our knowledge is unique. The dataset comprised of 263 images collected from a real greenhouse. Additionally, we applied a baseline Faster-RCNN object detector for the localization and classification lesions. Our experiments include (i) a version for the RGB channels and (ii) a custom backbone architecture version for feature fusion using the same Faster-RCNN head. Lastly, based on the detector{\textquoteright}s output, we compute an \>0.9 F1-score on binary classification, while mAP 18.5\% and mAP 20.2\% on detection, highlight the added value of NIR spectral bands for detecting these diseases.}, keywords = {Faster-RCNN, Feature fusion, Leaf Miner, Plant Disease Dataset, Powdery Mildew}, issn = {2772-3755}, doi = {https://doi.org/10.1016/j.atech.2022.100146}, url = {https://www.sciencedirect.com/science/article/pii/S2772375522001101}, author = {P.S. Georgantopoulos and D. Papadimitriou and C. Constantinopoulos and T. Manios and I.N. Daliakopoulos and D. Kosmopoulos} } @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} } @proceedings {146, title = {PupilTAN: A Few-Shot Adversarial Pupil Localizer}, journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, GAZE 2021: Gaze Estimation and Prediction in the Wild }, year = {2021}, pages = {3134-3142}, publisher = {BEST PAPER AWARD}, abstract = {

The eye center localization is a challenging problem faced by many computer vision applications. The challenges typically stem from the scene variability, such as, the wide range of shapes, the lighting conditions, the view angles and the occlusions. Nowadays, the increasing interest on deep neural networks requires a large volume of training data. However, a significant issue is the dependency on labeled data, which are expensive to obtain and susceptible to errors. To address these issues, we propose a deep network, dubbed PupilTAN, that performs image to-heatmap Translation and an Adversarial training framework that solves the eye localization problem in a few-shot unsupervised way. The key idea is to estimate, by using only a few ground-truth shots, the heatmaps centers{\textquoteright} pdf and use it as a generator to create random heatmaps that follow the same probability distribution of the real ones. We showcase that training the deep network with these artificial heatmaps in an adversarial framework not only makes us less dependent on labeled data, but also leads to a significant accuracy improvement. The proposed network achieves realtime performance in a general-purpose computer environment and improves the state-of-the-art accuracy for both MUCT and BioID datasets, even compared with supervised techniques. Furthermore, our model is robust even in the case of reducing its size of up to 1/16 of the original network (0.2M parameters), demonstrating comparable accuracy to the state-of-the-art with high practical value to real-time applications.

}, author = {Nikolaos Poulopoulos and Emmanouil Z. Psarakis and Dimitrios Kosmopoulos} } @proceedings {149, title = {Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation}, journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision}, year = {2021}, pages = {11946-11955}, abstract = {Automating sign language translation (SLT) is a challenging real-world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the availability of laborious to obtain gloss sequence groundtruth. In this paper, we attenuate this need, by introducing an end-to-end SLT model that does not entail explicit use of glosses; the model only needs text groundtruth. This is in stark contrast to existing end-to-end models that use gloss sequence groundtruth, either in the form of a modality that is recognized at an intermediate model stage, or in the form of a parallel output process, jointly trained with the SLT model. Our approach constitutes a Transformer network with a novel type of layers that combines: (i) local winner-takes-all (LWTA) layers with stochastic winner sampling, instead of conventional ReLU layers, (ii) stochastic weights with posterior distributions estimated via variational inference, and (iii) a weight compression technique at inference time that exploits estimated posterior variance to perform massive, almost lossless compression. We demonstrate that our approach can reach the currently best reported BLEU-4 score on the PHOENIX 2014T benchmark, but without making use of glosses for model training, and with a memory footprint reduced by more than 70\%.}, author = {Andreas Voskou and Konstantinos Panousis and Dimitrios Kosmopoulos and Dimitris Metaxas and Sotirios Chatzis} } @proceedings {135, title = {A Dataset of Mycenaean Linear B Sequences}, journal = {Language Resources and Evaluation Conference - LREC}, year = {2020}, pages = {2552-2561}, abstract = {[size= 13.008px]We present a dataset of Mycenaean Linear B sequences gathered from the Mycenaean inscriptions written in the 13th and 14th century B.C. (c. 1400-1200 B.C.). The dataset contains sequences of Mycenaean words and ideograms according to the rules of the Mycenaean Greek language in the Late Bronze Age. Our ultimate goal is to contribute to the study, reading and understanding of ancient scripts and languages. Focusing on sequences, we seek to exploit the structure of the entire language, not just the Mycenaean vocabulary, to analyse sequential patterns. We present an initial experiment on estimating the missing symbols in damaged inscriptions using the dataset.[/size]}, author = {K. Papavasileiou and G. Owens and D. Kosmopoulos} } @proceedings {145, title = {Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation}, journal = {International Symposium on Visual Computing}, year = {2020}, pages = {251-262}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf. In this work, we introduce a paradigm of designing recurrent deep networks whereby the output of the recurrent layer is derived from appropriate arguments from nonparametric statistics. A novel variational Bayesian sequence-to-sequence network architecture is proposed that consists of a) a full Gaussian posterior distribution for data-driven memory compression and b) a nonparametric Indian Buffet Process prior for regularization applied on the Gated Recurrent Unit non-gate weights. We dub our approach Stick-Breaking Recurrent network and show that it can achieve a substantial weight compression without diminishing modeling performance.}, isbn = {978-3-030-64559-5}, author = {Partaourides, Harris and Voskou, Andreas and Kosmopoulos, Dimitrios and Chatzis, Sotirios and N. Metaxas, Dimitris} } @article {129, title = {A framework for online segmentation and classification of modeled actions performed in the context of unmodeled ones}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, volume = {27}, year = {2017}, pages = {2578-2590}, abstract = {In this work, we propose a discriminative framework for online simultaneous segmentation and classification of modeled visual actions that can be performed in the context of other, unknown actions.To this end, we employ Hough transform to vote in a 3D space for the begin point, the end point and the label of the segmented part of the input stream. An SVM is used to model each class and to suggest putative labeled segments on the timeline.To identify the most plausible segments among the putative ones we apply a dynamic programming algorithm, which maximizes the likelihood for label assignment in linear time. The performance of our method is evaluated on synthetic, as well as on real data (Weizmann, TUM Kitchen, UTKAD and Berkeley multimodal human action databases). Extensive quantitative results obtained on a number of standard datasets demonstrate that the proposed approach is of comparable accuracy to the state of the art for online stream segmentation and classification when all performed actions are known and performs considerably better in the presence of unmodeled actions.}, author = {Dimitrios Kosmopoulos and Konstantinos Papoutsakis and Antonis Argyros} } @proceedings {127, title = {A Nonstationary Hidden Markov ModelWith Approximately Infinitely-Long Time-Dependencies}, journal = {International Symposium on Visual Computing}, volume = {II}, year = {2014}, pages = {51-62}, edition = {LNCS, Advances in Visual Computing}, abstract = {

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependencies, namely the sequence memoizer. We manage to derive training and inference algorithms for our model with computational costs identical to simple first-order HMMs, despite its entailed infinitely-long time-dependencies, by employing a mean-field-like approximation.The efficacy of our proposed model is experimentally demonstrated.

}, author = {Sotirios Chatzis and Dimitrios Kosmopoulos and George Papadourakis} } @proceedings {125, title = {Online segmentation and classification of modeled actions performed in the context of unmodeled ones}, journal = {British Machine Vision Conference}, year = {2014}, abstract = {In this work, we provide a discriminative framework for online simultaneous segmentation and classification of visual actions, which deals effectively with unknown sequences that may interrupt the known sequential patterns. To this end we employ Hough transform to vote in a 3D space for the begin point, the end point and the label of the segmented part of the input stream. An SVM is used to model each class and to suggest putative labeled segments on the timeline. To identify the most plausible segments among the putative ones we apply a dynamic programming algorithm, which maximises an objective function for label assignment in linear time. The performance of our method is evaluated on synthetic as well as on real data (Weizmann and Berkeley multimodal human action database). The proposed approach is of comparable accuracy to the state of the art for online stream segmentation and classification and performs considerably better in the presence of previously unseen actions.}, author = {Dimitrios Kosmopoulos and Kostas Papoutsakis and Antonis Argyros} } @inbook {124, title = {Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients}, booktitle = {Artificial Intelligence: Methods and Applications}, series = {Lecture Notes in Computer Science}, volume = {8445}, year = {2014}, pages = {406-417}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {A method using Zernike Moments and Histogram of Oriented Gradients for classification of plant leaf images is proposed in this paper. After preprocessing, we compute the shape features of a leaf using Zernike Moments and texture features using Histogram of Oriented Gradients and then the Sup-port Vector Machine classifier is used for plant leaf image classification and recognition. Experimental results show that using both Zernike Moments and Histogram of Oriented Gradients to classify and recognize plant leaf image yields accuracy that is comparable or better than the state of the art. The method has been validated on the Flavia and the Swedish Leaves datasets as well as on a combined dataset.}, keywords = {Histogram of oriented gradients, Leaf recognition, Support vector machine, Zernike moments}, isbn = {978-3-319-07063-6}, doi = {10.1007/978-3-319-07064-3_33}, url = {http://dx.doi.org/10.1007/978-3-319-07064-3_33}, author = {Tsolakidis, DimitrisG. and Kosmopoulos, DimitriosI. and Papadourakis, George}, editor = {Likas, Aristidis and Blekas, Konstantinos and Kalles, Dimitris} } @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 {31, title = {A hierarchical feature fusion framework for adaptive visual tracking}, journal = {Image and Vision Computing}, volume = {29}, year = {2011}, pages = {594 - 606}, keywords = {Particle filter, Sequential Monte-Carlo, Visual tracking}, issn = {0262-8856}, doi = {10.1016/j.imavis.2011.07.001}, author = {Alexandros Makris and Dimitrios Kosmopoulos and Stavros Perantonis and Sergios Theodoridis} } @article {26, title = {Multimodal and ontology-based fusion approaches of audio and visual processing for violence detection in movies}, journal = {Expert Systems with Applications}, volume = {38}, year = {2011}, pages = {14102 - 14116}, keywords = {Knowledge representation, Learning, Movie, Multimodal fusion, Ontology, Reasoning, Violence}, issn = {0957-4174}, doi = {10.1016/j.eswa.2011.04.219}, author = {Thanassis Perperis and Theodoros Giannakopoulos and Alexandros Makris and D. Kosmopoulos and Sofia Tsekeridou and Stavros J. Perantonis and Sergios Theodoridis} } @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} } @article {28, title = {Detecting abnormal human behaviour using multiple cameras}, journal = {Signal Processing}, volume = {89}, year = {2009}, pages = {1723 - 1738}, keywords = {Behaviour understanding, hidden Markov model, Homography, Support vector machine, Trajectory}, issn = {0165-1684}, doi = {10.1016/j.sigpro.2009.03.016}, author = {Panagiota Antonakaki and Dimitrios Kosmopoulos and Stavros J. Perantonis} } @proceedings {4, title = {Hierarchical Feature Fusion for Visual Tracking}, journal = {IEEE Int Conf on Image Processing}, year = {2007}, pages = {289-292}, author = {Makris, A. and D. Kosmopoulos and Perantonis, S.J. and Theodoridis, S.} }