@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 {147, title = {A Method for Recovering Near Infrared Information from RGB Measurements with Application in Precision Agriculture}, journal = {European Signal Processing Conference}, year = {2021}, pages = {616-620}, publisher = {IEEE}, edition = {IEEE}, abstract = {[size= 13.008px]In this work we develop a cost-efficient coupled dictionary learning based method for reconstructing multispectral images using only a single RGB commercial camera, without requiring the sensitivity function of the camera sensor. Considering the very high cost, the acquisition time and reduced mobility of multispectral cameras we claim that this is a very attractive option. In contrast to other approaches, the proposed method is not limited only to spectral bands inside the visible spectrum, but it also considers an even more challenging task, that is the reconstruction of spectral bands outside the visible range closer to the near-infrared wavelengths of the spectrum. Extensive experiments with real data demonstrate the effectiveness and applicability of the proposed method in the precision agriculture domain. To this end, we calculate one of the most widely used vegetation indices, the normalized difference vegetation index (NVDI), which may be used for plant health monitoring.[/size]}, author = {A. Gkillas and D. Kosmopoulos and C. Constantinopoulos and D. Ampeliotis and K. Berberidis} } @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} } @proceedings {133, title = {Translation of Sign Language Glosses to Text Using Sequence-to-Sequence Attention Models}, journal = {The 15th International Conference on Signal Image Technology \& Internet based Systems - SITIS}, year = {2019}, month = {2019}, abstract = {This work deals with the problem of Sign Language Translation and more specifically with translating Glosses to text. We applied Sequence to Sequence models with attention mechanism to a parallel gloss to English corpus. This is the first work that used these models to translate American gloss sentences to English. We present our experiments on several network architectures with three different attention functions. The results are very promising and can be useful for the further implementation of a full sign language recognition system.}, author = {N. Arvanitis and C. Constantinopoulos and D. Kosmopoulos} } @article {122, title = {A Latent Manifold Markovian Dynamics Gaussian Process}, journal = {Neural Networks and Learning Systems, IEEE Transactions on}, volume = {26}, year = {2015}, month = {01/2015}, pages = {70-83}, abstract = {
In this paper, we propose a Gaussian process model for analysis of nonlinear time series. Formulation of our model is based on the consideration that the observed data are functions of latent variables, with the associated mapping between observations and latent representations modeled through Gaussian process priors. In addition, to capture the temporal dynamics in the modeled data, we assume that subsequent latent representations depend on each other on the basis of a hidden Markov prior imposed over them. Derivation of our model is performed by marginalizing out the model parameters in closed form by using Gaussian process priors for observation mappings, and appropriate stick-breaking priors for the latent variable (Markovian) dynamics. This way, we eventually obtain a nonparametric Bayesian model for dynamical systems that accounts for uncertainty in the modeled data. We provide efficient inference algorithms for our model on the basis of a truncated variational Bayesian approximation. We demonstrate the efficacy of our approach considering a number of applications dealing with real-world data, and compare it to related state-of-the-art approaches.
}, keywords = {Gaussian process, latent manifold, Markovian dynamics, stick-breaking process, variational Bayes}, author = {Sotirios Chatzis and Dimitrios Kosmopoulos} } @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 {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 {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 {38, title = {A Conditional Random Field-Based Model for Joint Sequence Segmentation and Classification}, journal = {Pattern Recognition}, volume = {46}, year = {2013}, month = {06/2013}, pages = {1569-1578}, author = {Sotirios P. Chatzis and D. Kosmopoulos and P. Doliotis} } @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 {14, title = {Visual Workflow Recognition Using a Variational Bayesian Treatment of Multistream Fused Hidden Markov Models}, journal = {Circuits and Systems for Video Technology, IEEE Transactions on}, volume = {22}, year = {2012}, month = {July}, pages = {1076 -1086}, keywords = {active learning-based visual workflow recognition, Bayes methods, classification variance, data labeling, hidden Markov models, image sequences, learned model, learning (artificial intelligence), maximum a posteriori training, maximum information gain method, maximum likelihood training, MFHMM parameters, model variance, multistream fused hidden Markov models, overfitting issues, point estimate-based methods, posterior distribution, training methods, unlabeled data, variational Bayesian treatment, video streaming, video surveillance}, issn = {1051-8215}, doi = {10.1109/TCSVT.2012.2189795}, author = {Sotirios P. Chatzis and D. Kosmopoulos} } @article {24, title = {A variational Bayesian methodology for hidden Markov models utilizing Student{\textquoteright}s-t mixtures}, journal = {Pattern Recognition}, volume = {44}, year = {2011}, pages = {295 - 306}, keywords = {Violence detection}, issn = {0031-3203}, doi = {10.1016/j.patcog.2010.09.001}, author = {Sotirios P. Chatzis and D. Kosmopoulos} } @article {5, title = {Robust Visual Behavior Recognition}, journal = {Signal Processing Magazine, IEEE}, volume = {27}, year = {2010}, month = {Sep.}, pages = {34-45}, issn = {1053-5888}, author = {D. Kosmopoulos and Sotirios P. Chatzis} } @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.} } @article {27, title = {Vision-based production of personalized video}, journal = {Signal Processing: Image Communication}, volume = {24}, year = {2009}, pages = {158 - 176}, keywords = {Automated content production, Human identification, tracking}, issn = {0923-5965}, doi = {10.1016/j.image.2008.12.010}, author = {D.I. Kosmopoulos and A. Doulamis and Makris, A. and N. Doulamis and Sotirios P. Chatzis and S.E. Middleton} } @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.} }