@conference {179,
	title = {Geometric Knowledge Distillation via Procrustes Analysis for Efficient Motion Sequence Classification},
	booktitle = {2025 25th International Conference on Digital Signal Processing (DSP)},
	year = {2025},
	month = {June 25{\textendash}27},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Costa Navarino, Messinia, Greece},
	abstract = {Motion sequence classification methods that rely on geometric approaches{\textemdash}such as Procrustes analysis and Dynamic Time Warping (DTW){\textemdash}offer high accuracy but are often unsuitable for real-time applications due to their computational cost. In this paper, we present a novel geometric knowledge distillation framework that bridges the gap between accuracy and efficiency by transferring rich geometric insights from a Procrustes-DTW-based distance metric into a transformer-based neural network. By generating soft probability distributions from pre-computed Procrustes-DTW distances, we effectively guide the student model{\textquoteright}s training to preserve essential geometric properties like shape similarity, temporal alignment, and spatial transformation invariance. Our method enables fast and scalable motion sequence classification while retaining the benefits of geometric interpretability. We evaluate our framework on two benchmark tasks: sign language recognition using the SIGNUM dataset and human action recognition on UTD-MHAD. Results show that our distillation approach significantly improves classification accuracy over standard supervised learning and achieves dramatically lower inference time compared to traditional geometric methods{\textemdash}making it ideal for real-time motion understanding in wearable, robotic, and interactive systems.},
	keywords = {Action Recognition, Efficient Inference, Geometric Distance Learning, Knowledge Distillation, Procrustes Analysis, Sign Language Recognition, Skeletal Sequences},
	author = {De, Bikram and Blekos, Kostas and Pikoulis, Vasilis and Kosmopoulos, Dimitrios and Metsis, Vangelis}
}
@article {173,
	title = {Guest Editorial: New Frontiers in Image and Video Processing for Sustainable Agriculture},
	journal = {IET Image Processing},
	volume = {19},
	year = {2025},
	pages = {e70032},
	keywords = {agricultural engineering, computer vision, image classification, image processing, object detection, remote sensing},
	doi = {https://doi.org/10.1049/ipr2.70032},
	url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/ipr2.70032},
	author = {Moroni, Davide and Kosmopoulos, Dimitrios}
}
@article {162,
	title = {A generative model for the Mycenaean Linear B script and its application in infilling text from ancient tablets},
	journal = {ACM Journal on Computing and Cultural Heritage},
	volume = {16},
	year = {2023},
	pages = {1-25},
	abstract = {We present a generative neural language model for the most ancient proven stage of the Greek language, the Mycenaean Greek attributed by the Linear B script. To capture the statistical structure of the Mycenaean documents we present a Bidirectional Recurrent Neural Network and compare it to the traditionally used n-grams. The model is used to supplement the damaged parts of the Mycenaean texts, namely the incomplete, to a greater or lesser extent, words, which are typically discovered on partially damaged clay tablets. We verify our method experimentally using ground - truth, then we demonstrate our results on real cases and compare with experts{\textquoteright} opinions. We also present a methodology to augment our dataset, which turns out to improve our results.},
	author = {K. PAPAVASSILEIOU and Dimitrios I. Kosmopoulos}
}
@proceedings {19,
	title = {Gesture-based video summarization},
	journal = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {3},
	year = {2005},
	month = {Sept.},
	pages = {1220-1223},
	keywords = {gesture energy, gesture-based video summarization, gestures extraction, image colour analysis, image segmentation, key-frames extraction, sign language videos, skin color segmentation, visual events, Zernike moments},
	doi = {10.1109/ICIP.2005.1530618},
	author = {D. Kosmopoulos and Doulamis, A.D. and N. Doulamis}
}
