@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 \&gt;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 {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 = {<span style="caret-color: $\#$333333; color: $\#$333333; font-family: {\textquoteright}Helvetica Neue{\textquoteright}, Helvetica, Arial, sans-serif; font-size: 12px; background-color: $\#$98d4ee;">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\%.</span>},
	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 {87,
	title = {Non-invasive analysis of sleep patterns via multimodal sensor input},
	journal = {Personal and Ubiquitous Computing},
	volume = {18},
	year = {2014},
	pages = {19-26},
	doi = {10.1007/s00779-012-0623-1},
	author = {Vangelis Metsis and Dimitrios Kosmopoulos and Vassilis Athitsos and Fillia Makedon}
}
@proceedings {89,
	title = {Robust offline topological map estimation using visual loop closures},
	journal = {6th International Conference on Pervasive Technoogies for Assistive Environments},
	year = {2013},
	author = {Dimitrios Kosmopoulos and Ilias Maglogiannis and Fillia Makedon}
}
@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.}
}
@proceedings {84,
	title = {A method for online analysis of structured processes using bayesian filters and echo state networks},
	journal = {Proceedings of the 12th European Conference on Computer Vision - Volume Part III},
	year = {2012},
	pages = {71-80},
	publisher = {Springer-Verlag},
	address = {Florence - Italy},
	isbn = {978-3-642-33884-7},
	author = {Kosmopoulos, Dimitrios I. and Makedon, Fillia}
}
@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 {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 {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.}
}
@article {36,
	title = {Artificial Intelligence Systems for the Characterization of Digital Images Containing Pigmented Skin Lesions: Applications in the Detection of Malignant Melanoma},
	journal = {Oncology Reports},
	volume = {15},
	year = {2006},
	pages = {1027-1032},
	author = {Maglogiannis, Ilias and D. Kosmopoulos}
}
@article {37,
	title = {Hand Tracking for Gesture Recognition Tasks using Dynamic Bayesian Network},
	journal = {International Journal of Intelligent Systems and Applications},
	volume = {1},
	year = {2006},
	pages = {359-375},
	author = {D. Kosmopoulos and Maglogiannis, Ilias}
}
@article {35,
	title = {A system for the acquisition of reproducible digital skin lesions images},
	journal = {Technology and Health Care},
	volume = {11},
	year = {2004},
	pages = {425{\textendash}441},
	keywords = {camera calibration, color constancy, image analysis, reproducible images, skin lesion inspection, telemedicine},
	issn = {0928-7329},
	author = {Maglogiannis, Ilias and Kosmopoulos, Dimitrios I.}
}
