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Ioannis Pitas
Researcher at Aristotle University of Thessaloniki
Publications - 826
Citations - 26338
Ioannis Pitas is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Facial recognition system & Digital watermarking. The author has an hindex of 76, co-authored 795 publications receiving 24787 citations. Previous affiliations of Ioannis Pitas include University of Bristol & University of York.
Papers
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Journal ArticleDOI
Human Action Recognition Based on Multi-View Regularized Extreme Learning Machine
TL;DR: An extension of the Extreme Learning Machine algorithm that is able to exploit multiple action representations and scatter information in the corresponding ELM spaces for the calculation of the networks’ parameters and the determination of optimized network combination weights is proposed.
Proceedings ArticleDOI
Support vector machines on the space of Walsh functions and their properties
TL;DR: This paper constructs a new kernel function for support vector machine, which is based on Walsh functions, and proves some theoretical results related to the VC-dimension of the support vector machines which are built in the space of the Walsh functions.
Proceedings ArticleDOI
Distributed, MapReduce-Based Nearest Neighbor and E-Ball Kernel k-Means
TL;DR: This paper presents a MapReduce based distributed implementation of Nearest Neighbor and ε-ball Kernel k-Means, a state of the art clustering algorithm which employs the kernel trick, in order to perform clustering on a higher dimensionality space, thus overcoming the limitations of classic k- means regarding the non linear separability of the input data.
Proceedings ArticleDOI
Iterative Label Propagation on facial images
TL;DR: Experimental results showed that the proposed Iterative Label Propagation (ILP) method outperforms state of the art methods either when only one or both video channels are used for label propagation.
Book ChapterDOI
Nonlinear mean filters and their applications in image filtering and edge detection
TL;DR: In this paper, a general filter structure encompassing the filters that are based on order statistics, the homomorphic filters, the nonlinear mean filters, and the morphological filters is presented.