@proceedings {155,
	title = {Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture Classification},
	journal = {International Joint Conference on Computer Vision Imaging and Computer Graphics Theory and Applications},
	year = {2022},
	abstract = {<span>The success of deep learning in recent years relies on the availability of large amounts of accurately annotated training data. In this work, we investigate a technique for utilizing automatically annotated data in classification problems. Using a small number of manually annotated samples, and a large set of data that feature automatically created, noisy labels, our approach trains a Convolutional Neural Network (CNN) in an iterative manner. The automatic annotations are combined with the predictions of the network in order to gradually expand the training set. In order to evaluate the performance of the proposed approach, we apply it to the problem of hand posture recognition from RGB images. We compare the results of training a CNN classifier with and without the use of our technique. Our method yields a significant increase in average classification accuracy, and also decreases the deviation in class accuracies, thus indicating the validity and the usefulness of the proposed approach.</span>},
	author = {Georgios Lydakis and Iason Oikonomidis and Dimitrios Kosmopoulos and Antonis Argyros}
}
@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 {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}
}
