Color Texture Segmentation by Decomposition of Gaussian Mixture Model

0041781 20240103182809.520070119235959.9 Color Texture Segmentation by Decomposition of Gaussian Mixture Model 10 s. cav_un_epca*02585180302-9743Lecture Notes in Computer Science194225 (2006)287-296 Segmentace barevné textury dekompozicí modelu textury ve tvaru normální směsi texture segmentation gaussian mixture model EM algorithm cav_un_auth*0101091 Grim Jiří UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101197 Somol Petr UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101093 Haindl Michal UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101182 Pudil Pavel UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/historie/grim-color texture segmentation by decomposition of gaussian mixture model.pdf 09J 12B 09K 1ET400750407 GA AV ČR cav_un_auth*0001797 507752 XE EC cav_un_auth*0200689 1M0572 GA MŠk cav_un_auth*0001814 2C06019 GA MŠk cav_un_auth*0216518 CEZ:AV0Z10750506 Recently we have proposed Gaussian mixtures as a local statistical model to synthesize artificial textures. We describe the statistical dependence of pixels of a movable window by multivariate Gaussian mixture of product components. The mixture components correspond to different variants of image patches as they appear in the window. In this sense they can be used to identify different segments of the source color texture image. The segmentation can be obtained by means of Bayes formula provided that a proper decomposition of the estimated Gaussian mixture into sub-mixtures is available. In this paper the mixture model is decomposed by maximizing the mean probability of correct classification of pixels into segments in a way taking into account the assumed consistency of final segmentation. Metoda je založena na lokálním statistickém modelu textury ve tvaru normální distribuční směsi, která popisuje statistické závislosti pixelů v rozsahu zvoleného pohyblivého okna. V práci je navržen algoritmus umožňující dekompozici směsi na části popisující jednotlivé segmenty textury. cav_un_auth*0217721 Iberoamerican Congress on Pattern Recognition. CIARP 2006 /11./ Cancun 14.11.2006-17.11.2006 MX 2007 IN http://hdl.handle.net/11104/0135155 2006 cav_un_epca*0258518 Lecture Notes in Computer Science 0302-9743 Roč. 19 č. 4225 2006 287 296