@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}
}
@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}
}
@proceedings {125,
	title = {Online segmentation and classification of modeled actions performed in the context of unmodeled ones},
	journal = {British Machine Vision Conference},
	year = {2014},
	abstract = {In this work, we provide a discriminative framework for online simultaneous segmentation
and classification of visual actions, which deals effectively with unknown sequences
that may interrupt the known sequential patterns. 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 maximises
an objective function for label assignment in linear time. The performance of our method
is evaluated on synthetic as well as on real data (Weizmann and Berkeley multimodal human
action database). The proposed approach is of comparable accuracy to the state
of the art for online stream segmentation and classification and performs considerably
better in the presence of previously unseen actions.},
	author = {Dimitrios Kosmopoulos and Kostas Papoutsakis and Antonis Argyros}
}
@proceedings {90,
	title = {Developing visual competencies for  socially assistive robots: the HOBBIT approach},
	journal = {6th International Conference on Pervasive Technologies for Assistive Environments},
	year = {2013},
	abstract = {In this paper, we present our approach towards developing visual competencies for socially assistive robots within the framework of the HOBBIT project. We show how we integrated several vision modules using a layered architectural scheme. Our goal is to endow the mobile robot with visual perception capabilities so that it can interact with the users. We present the key modules of independent motion detection, object detection, body localization, person tracking, head pose estimation and action recognition and we explain how they serve the goal of natural integration of robots in social environments.},
	author = {Kostas Papoutsakis and Pashalis Panteleris and Antonis Ntelidakis and Stavros Stefanou and Xenofon Zabulis and Dimitrios Kosmopoulos and Antonis Argyros}
}
