@conference {167,
	title = {Procrustes-DTW: Dynamic Time Warping Variant for the Recognition of Sign Language Utterances},
	booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
	year = {2023},
	abstract = {[color=$\#$333333; font-family: {\textquoteright}HelveticaNeue Regular{\textquoteright}, sans-serif; font-size: 18px; font-variant-ligatures: normal; orphans: 2; widows: 2; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial]In this paper we present a method for classifying sign language videos using a variation of dynamic time warping with the Procrustes distance, which is a shape similarity measure appropriate for handshape recognition. We initially extract features from the dominant hand. We then classify the signs using a nearest-neighbor scheme which includes a dynamic time warping variant, which we dub Procrustes-DTW and which is suitable for extracting a similarity measure of sequences of handshapes only. To reduce computational costs it is combined with a curvature-based summarization scheme with a reasonable sacrifice in accuracy. We verify experimentally our approach on a custom dataset and we show its merit compared to the conventional dynamic time warping approach.[/color]},
	doi = {10.1109/ICASSPW59220.2023.10193012},
	author = {Arvanitis, Nikolaos and Sartinas, Evangelos and Kosmopoulos, Dimitrios}
}
@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}
}
@conference {85,
	title = {Violence content classification using audio features},
	booktitle = {Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence},
	year = {2006},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin, Heidelberg},
	isbn = {3-540-34117-X, 978-3-540-34117-8},
	author = {Giannakopoulos, Theodoros and Kosmopoulos, Dimitrios and Aristidou, Andreas and Theodoridis, Sergios}
}
