0411250 20240103182312.920060210235959.9 80-86732-18-5 1 Mathematics and Computer Science. Praha MFF UK 2003 5 s. 221-225ŠafránkováJ. texture unsupervised segmentation Markov random fields cav_un_auth*0101165 Mikeš Stanislav 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. 09K IST-2001-34744 Commission EC XE cav_un_auth*0200688 IAA2075302 GA AV ČR cav_un_auth*0001801 CEZ:AV0Z1075907 An efficient and robust type of unsupervised multispectral texture segmentation method is presented. The algorithm starts with spectral factorization of an input multispectral texture image using the Karhunen-Loeve expansion. Monospectral factors of single texture patches are assumed to be modelled using a Gaussian Markov random field model. The texture segmentation is done by K-means algorithm in the Markov model parameter space evaluated for each pixel centered image window. cav_un_auth*0213138 Week of Doctoral Students 2003. WDS'03 Praha CZ 10.06.2003-13.06.2003 BD RO http://hdl.handle.net/11104/0131335 UTIA-B 20030237 2003 2003 Praha MFF UK 80-86732-18-5 WDS '03 Proceedings of Contributed Papers 221 225 Šafránková J. 340