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Mathematical Methods for Signal and Image Analysis and Representation
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SYSNO ASEP 0374142 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Visual Data Recognition and Modeling Based on Local Markovian Models Author(s) Haindl, Michal (UTIA-B) RID, ORCID Number of authors 1 Source Title Mathematical Methods for Signal and Image Analysis and Representation, 14. - London : Springer London, 2012 / Florack Luc ; Duits Remco ; Jongbloed Geurt ; Lieshout Marie-Colette ; Davies Laurie - ISBN 978-1-4471-2353-8 Pages s. 241-259 Number of pages 19 s. Number of pages 317 Language eng - English Country GB - United Kingdom Keywords Markov random fields ; image modeling ; image recognition Subject RIV BD - Theory of Information R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GAP103/11/0335 GA ČR - Czech Science Foundation (CSF) GA102/08/0593 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) DOI 10.1007/978-1-4471-2353-8_14 Annotation An exceptional 3D wide-sense Markov model which can be completely solved analytically and easily synthesised is presented. The model can be modified to faithfully represent complex local data by adaptive numerically robust recursive estimators of all its statistics. Illumination invariants can be derived from some of its recursive statistics and exploited in content based image retrieval, supervised or unsupervised image recognition. Its modelling efficiency is demonstrated on several analytical and modelling image applications, in particular on unsupervised image or range data segmentation, bidirectional texture function (BTF) synthesis and compression, dynamic texture synthesis and adaptive multispectral and multichannel image and video restoration. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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