@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 {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 {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} } @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} } @article {11, title = {A system for multi-camera task recognition and summarization for structured environments}, journal = {Industrial Informatics, IEEE Transactions on}, volume = {9}, year = {2013}, pages = {161-171}, issn = {1551-3203}, doi = {10.1109/TII.2012.2212712}, author = {D. Kosmopoulos and A. Voulodimos and Doulamis, A.D.} } @article {2, title = {Bayesian Filter based Behavior Recognition in Workflows allowing for User Feedback}, journal = {Computer Vision and Image Understanding}, volume = {116}, year = {2012}, pages = {422-434}, abstract = {In this paper, we propose a novel online framework for behavior understanding, in visual workflows, capable of achieving high recognition rates in real-time. To effect online recognition, we propose a methodology that employs a Bayesian filter supported by hidden Markov models. We also introduce a novel re-adjustment framework of behavior recognition and classification by incorporating the user{\textquoteright}s feedback into the learning process through two proposed schemes: a plain non-linear one and a more sophisticated recursive one. The proposed approach aims at dynamically correcting erroneous classification results to enhance the behavior modeling and therefore the overall classification rates. The performance is thoroughly evaluated under real-life complex visual behavior understanding scenarios in an industrial plant. The obtained results are compared and discussed. }, author = {D. Kosmopoulos and N. Doulamis and A. Voulodimos} } @article {40, title = {Improving Multi-Camera Activity Recognition by Employing Neural Network Based Readjustment}, journal = {Applied Artificial Intelligence}, volume = {26}, year = {2012}, pages = {97-118}, doi = {10.1080/08839514.2012.629540}, author = {Voulodimos, Athanasios S. and Doulamis, Nikolaos D. and Kosmopoulos, Dimitrios I. and Varvarigou, Theodora A.} } @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.} } @article {9, title = {Online classification of visual tasks for industrial workflow monitoring}, journal = {Neural Networks}, volume = {24}, year = {2011}, pages = {852{\textendash}860}, keywords = {Activity recognition, ESN, Fusion, Genetic algorithm, HMM, Workflow}, issn = {0893-6080}, author = {Athanasios Voulodimos and D. Kosmopoulos and Veres, Galina and Helmut Grabner and Van Gool, Luc and Varvarigou, T.A.} } @proceedings {18, title = {Video summarization guiding evaluative rectification for industrial activity recognition}, journal = {Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on}, year = {2011}, month = {nov.}, pages = {950-957}, keywords = {activity recognition framework, evaluative rectification method, feature extraction, image recognition, industrial activity recognition, industrial surveillance video, key-frame extraction, neural nets, neural network, production engineering computing, video signal processing, video summarization}, doi = {10.1109/ICCVW.2011.6130354}, author = {Voulodimos, A.S. and Doulamis, A.D. and D. Kosmopoulos and Varvarigou, T.A.} } @proceedings {7, title = {Enhanced Human Behavior Recognition using HMM and Evaluative Rectification}, journal = {ACM Multimedia, ARTEMIS Workshop}, year = {2010}, author = {Doulamis, N.D. and Voulodimos, A.S. and D. Kosmopoulos and Varvarigou, T.A.} } @article {30, title = {Multiclass defect detection and classification in weld radiographic images using geometric and texture features}, journal = {Expert Systems with Applications}, volume = {37}, year = {2010}, pages = {7606 - 7614}, keywords = {Classification, Defects, Geometrical features, Radiography, Segmentation, Texture, Welds}, issn = {0957-4174}, doi = {10.1016/j.eswa.2010.04.082}, author = {Ioannis Valavanis and Dimitrios Kosmopoulos} } @proceedings {6, title = {Robust human behavior modeling from multiple cameras}, journal = {Pattern Recognition (ICPR), 20th International Conference on}, year = {2010}, pages = {3575-3578}, author = {D. Kosmopoulos and Voulodimos, A.S. and Varvarigou, T.A.} } @proceedings {8, title = {Robust Workflow Recognition Using Holistic Features and Outlier-Tolerant Fused Hidden Markov Models}, journal = {International Conference on Artificial Neural Networks}, volume = {1}, year = {2010}, pages = {551-560}, author = {Athanasios Voulodimos and Helmut Grabner and D. Kosmopoulos and Van Gool, Luc and Varvarigou, T.A.} } @article {3, title = {Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {31}, year = {2009}, pages = {1657-1669}, issn = {0162-8828}, author = {Sotirios P. Chatzis and D. Kosmopoulos and Varvarigou, T.A.} } @proceedings {15, title = {A robust approach towards sequential data modeling and its application in automatic gesture recognition}, journal = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on}, year = {2008}, month = {April}, pages = {1937-1940}, keywords = {automatic gesture recognition, data representation, finite Gaussian mixture model, Gaussian distribution, gesture recognition, hidden Markov model, hidden Markov models, hidden state distribution, maximum likelihood estimation, maximum likelihood framework, parameter estimation, sequential data modeling, student-t mixture model, time series, time series modeling}, doi = {10.1109/ICASSP.2008.4518015}, author = {Sotirios P. Chatzis and D. Kosmopoulos and Varvarigou, T.A.} } @article {12, title = {Signal Modeling and Classification Using a Robust Latent Space Model Based on t Distributions}, journal = {Signal Processing, IEEE Transactions on}, volume = {56}, year = {2008}, month = {March}, pages = {949 -963}, keywords = {Bayes methods, Bayesian inference, factor analysis modeling, inference mechanisms, robust latent space model, signal classification, signal modeling, signal processing, statistical distributions, t distribution, variational inference algorithm}, issn = {1053-587X}, doi = {10.1109/TSP.2007.907912}, author = {Sotirios P. Chatzis and D. Kosmopoulos and Varvarigou, T.A.} } @article {25, title = {MD-SIR: a methodology for developing sensor{\textemdash}guided industry robots}, journal = {Robotics and Computer-Integrated Manufacturing}, volume = {18}, year = {2002}, pages = {403 - 419}, keywords = {Application development, Control objects library, Open robots controller architecture, Sensor integration}, issn = {0736-5845}, doi = {10.1016/S0736-5845(02)00031-5}, author = {D. Kosmopoulos and Varvarigou, T.A. and D.M Emiris and A.A Kostas} } @article {29, title = {Automated inspection of gaps on the automobile production line through stereo vision and specular reflection}, journal = {Computers in Industry}, volume = {46}, year = {2001}, pages = {49 - 63}, keywords = {Automated visual inspection, Gap measurement, Specular reflection}, issn = {0166-3615}, doi = {10.1016/S0166-3615(01)00113-0}, author = {Dimitrios Kosmopoulos and Theodora Varvarigou} }