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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Journal ArticleDOI
Optimized transmission of JPEG2000 streams over wireless channels
TL;DR: The transmission of JPEG2000 images over wireless channels is examined using reorganization of the compressed images into error-resilient, product-coded streams which are shown to outperform other algorithms which were recently proposed for the wireless transmission of images.
Journal ArticleDOI
ECG pattern recognition and classification using non-linear transformations and neural networks: A review
Nicos Maglaveras,Telemachos Stamkopoulos,Konstantinos I. Diamantaras,Costas Pappas,Michael G. Strintzis +4 more
TL;DR: A generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques.
Book ChapterDOI
Semantic annotation of images and videos for multimedia analysis
Stephan Bloehdorn,Kosmas Petridis,Carsten Saathoff,Nikos Simou,Vassilis Tzouvaras,Yannis Avrithis,Siegfried Handschuh,Yiannis Kompatsiaris,Steffen Staab,Michael G. Strintzis +9 more
TL;DR: This paper uses M-OntoMat-Annotizer in order to construct ontologies that include prototypical instances of high-level domain concepts together with a formal specification of corresponding visual descriptors, allowing for new kinds of multimedia content analysis and reasoning.
Journal ArticleDOI
Use of depth and colour eigenfaces for face recognition
TL;DR: The proposed face recognition technique is based on the implementation of the principal component analysis algorithm and the extraction of depth and colour eigenfaces and Experimental results show significant gains attained with the addition of depth information.
Proceedings ArticleDOI
An ontology approach to object-based image retrieval
TL;DR: The proposed approach bridges the gap between keyword-based approaches, which assume the existence of rich image captions or require manual evaluation and annotation of every image of the collection, and query-by-example approaches,Which assume that the user queries for images similar to one that already is at his disposal.