M
Michael G. Strintzis
Researcher at Aristotle University of Thessaloniki
Publications - 240
Citations - 6529
Michael G. Strintzis is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Motion estimation & Image segmentation. The author has an hindex of 44, co-authored 240 publications receiving 6319 citations. Previous affiliations of Michael G. Strintzis include Information Technology Institute & University of Pittsburgh.
Papers
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Disparity and occlusion estimation in multiocular systems and their coding for the communication of multiview image sequences
TL;DR: An efficient disparity estimation and Occlusion detection algorithm for multiocular systems is presented and techniques are developed for the coding of occlusion and disparity information, which is needed at the receiver for the reproduction of a multiview sequence using the two encoded extreme views.
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Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks
TL;DR: Two approaches for ultrasonic image processing are examined and a modification of the learning vector quantizer (L(2 ) LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L(2) mean instead of the sample arithmetic mean of the input observations.
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3D model search and retrieval using the spherical trace transform
Dimitrios Zarpalas,Petros Daras,Apostolos Axenopoulos,Dimitrios Tzovaras,Michael G. Strintzis +4 more
TL;DR: The experimental results show that the proposed method is superior in terms of precision versus recall and can be used for 3D model search and retrieval in a highly efficient manner.
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Region-based image retrieval using an object ontology and relevance feedback
TL;DR: An image retrieval methodology suited for search in large collections of heterogeneous images is presented, which employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities.
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Evaluation of multiresolution block matching techniques for motion and disparity estimation
TL;DR: Multiresolution block matching methods for both monocular and stereoscopic image sequence coding are evaluated to drastically reduce the amount of processing needed for block correspondence without seriously affecting the quality of the reconstructed images.