@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 {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}
}
@inbook {124,
	title = {Plant Leaf Recognition Using Zernike Moments and Histogram of Oriented Gradients},
	booktitle = {Artificial Intelligence: Methods and Applications},
	series = {Lecture Notes in Computer Science},
	volume = {8445},
	year = {2014},
	pages = {406-417},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {A method using Zernike Moments and Histogram of Oriented Gradients for classification of plant leaf images is proposed in this paper. After preprocessing, we compute the shape features of a leaf using Zernike Moments and texture features using Histogram of Oriented Gradients and then the Sup-port Vector Machine classifier is used for plant leaf image classification and recognition. Experimental results show that using both Zernike Moments and Histogram of Oriented Gradients to classify and recognize plant leaf image yields accuracy that is comparable or better than the state of the art. The method has been validated on the Flavia and the Swedish Leaves datasets as well as on a combined dataset.},
	keywords = {Histogram of oriented gradients, Leaf recognition, Support vector machine, Zernike moments},
	isbn = {978-3-319-07063-6},
	doi = {10.1007/978-3-319-07064-3_33},
	url = {http://dx.doi.org/10.1007/978-3-319-07064-3_33},
	author = {Tsolakidis, DimitrisG. and Kosmopoulos, DimitriosI. and Papadourakis, George},
	editor = {Likas, Aristidis and Blekas, Konstantinos and Kalles, Dimitris}
}
@article {21,
	title = {A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments},
	journal = {MultiMedia, IEEE},
	volume = {19},
	year = {2012},
	month = {July-Sept.},
	pages = {42 -52},
	issn = {1070-986X},
	doi = {10.1109/MMUL.2012.31},
	author = {Athanasios Voulodimos and D. Kosmopoulos and Vasileiou, Georgios and Sardis, Emmanuel and Anagnostopoulos, Vasileios and Lalos, Constantinos and Doulamis, A.D. and Varvarigou, T.A.}
}
@proceedings {10,
	title = {A Dataset for Workflow Recognition in Industrial Scenes},
	journal = {IEEE International Conference on Image Processing},
	year = {2011},
	pages = {3310-3313},
	author = {A. Voulodimos and D. Kosmopoulos and Vasileiou, G. and Sardis, E. and Doulamis, A.D. and Anagnostopoulos, V. and Lalos, C. and Varvarigou, T.A.}
}
