@article {132,
	title = {Facilitation of Air Traffic Control via OCR-based Aircraft Registration Number Extraction},
	journal = {IET Intelligent Transportation Systems},
	volume = {12},
	year = {2018},
	pages = {965-975},
	abstract = {<p class="p1">To identify any aircraft in the world, it is sufficient to read its registration number. This number is a unique identifier, and offers valuable information, in the same way a car registration number does. In this work we present the results of our feasibility study towards a simple, yet very efficient and effective system to identify aircrafts using video-Optical Character Recognition acquired by off-the-shelf cameras. We used several videos under realistic conditions at the Heraklion airport during high-season and we achieved very promising results. We claim that there is much room for the development of a low-cost airport surface monitoring system based on standard cameras, which can complement high-cost radars.</p>},
	author = {Dimitrios G. Vidakis and Dimitrios I. Kosmopoulos}
}
@article {129,
	title = {A framework for online segmentation and classification of modeled actions performed in the context of unmodeled ones},
	journal = {IEEE Transactions on Circuits and Systems for Video Technology},
	volume = {27},
	year = {2017},
	pages = {2578-2590},
	abstract = {In this work, we propose a discriminative framework for online simultaneous segmentation and classification of modeled visual actions that can be performed in the context of other, unknown actions.To this end, we employ Hough transform to vote in a 3D space for the begin point, the end point and the label of the segmented part of the input stream. An SVM is used to model each class and to suggest putative labeled segments on the timeline.To identify the most plausible segments among the putative ones we apply a dynamic programming algorithm, which maximizes the likelihood for label assignment in linear time. The performance of our method is evaluated on synthetic, as well as on real data (Weizmann, TUM Kitchen, UTKAD and Berkeley multimodal human action databases). Extensive quantitative results obtained on a number of standard datasets demonstrate that the proposed approach is of comparable accuracy to the state of the art for online stream segmentation and classification when all performed actions are known and performs considerably better in the presence of unmodeled actions.},
	author = {Dimitrios Kosmopoulos and Konstantinos Papoutsakis and Antonis Argyros}
}
