@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}
}
@proceedings {134,
	title = {Towards a visual Sign Language dataset for home care services},
	journal = {15th IEEE International Conference on Face and Gesture Recognition},
	volume = {1},
	year = {2020},
	month = {2020},
	pages = {622-626},
	abstract = {[size= 13.008px]We present our work towards creating a dataset, which is intended to be used for the implementation of a home care services system for the deaf. The dataset includes recorded realistic scenarios of interactions between deaf patients and mental health experts in their native sign language. The scenarios allow for contextualized representations, in contrast to typical datasets presenting isolated signs or sentences. It includes continuous videos in RGB and depth, which are challenging to analyze and closely resemble real-life scenarios. The research on representation of signs is supported by providing the hand shapes and trajectories for every video using hand and skeleton models, as well as facial features. Furthermore, the dataset may be used for the study of emotional context in Sign Language, since such conversations are typically emotionally charged. [/size]},
	author = {D. Kosmopoulos and I. Oikonomidis and K. Konstantinopoulos and N. Arvanitis and K. Antzakas and A. Bifis and G. Lydakis and A. Roussos and A. Argyros}
}
@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 {123,
	title = {A top-down event-driven approach for concurrent activity recognition},
	journal = {Multimedia Tools Appl.},
	volume = {69},
	year = {2014},
	pages = {293-311},
	abstract = {<p><span>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.</span></p>},
	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 {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.}
}
