0374142 20240103200603.620120302235959.9 10.1007/978-1-4471-2353-8_14 19 s. 317 cav_un_epca*0374141978-1-4471-2353-814Computational Imaging and Vision241-259LondonSpringer London2012FlorackLucDuitsRemcoJongbloedGeurtLieshoutMarie-ColetteDaviesLaurie Markov random fields image modeling image recognition cav_un_auth*0101093 Haindl Michal Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/RO/haindl-0374142.pdf 1M0572 GA MŠk cav_un_auth*0001814 387/2010 CESNET CZ GAP103/11/0335 GA ČR cav_un_auth*0273627 GA102/08/0593 GA ČR cav_un_auth*0239567 CEZ:AV0Z10750506 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. 2012 BD 1 4 A 4a 20231122134947.5 http://hdl.handle.net/11104/0207127 2012 Soubory v repozitáři: haindl-0374142.pdf cav_un_epca*0374141 Mathematical Methods for Signal and Image Analysis and Representation 14 978-1-4471-2353-8 241 259 London Springer London 2012 Computational Imaging and Vision Florack Luc 340 Duits Remco 340 Jongbloed Geurt 340 Lieshout Marie-Colette 340 Davies Laurie 340