0359021 20240103195121.820110510235959.9 Praha MFF UK 2010 196 s. iamge segmentation Markov random fields cav_un_auth*0101165 Mikeš Stanislav Rozpoznávání obrazu Department of Pattern Recognition RO RO UTIA-B Department of Pattern Recognition Ústav teorie informace a automatizace AV ČR, v. v. i. 1M0572 GA MŠk cav_un_auth*0001814 GA102/08/0593 GA ČR cav_un_auth*0239567 2C06019 GA MŠk CZ cav_un_auth*0216518 507752 XE EC cav_un_auth*0200689 CEZ:AV0Z10750506 Image segmentation is a fundamental part in low level computer vision processing. It has an essential in uence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-dened problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random eld texture models (GMRF, 2D/3D CAR) were developed. These segmenters use e cient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative stateof- the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modied EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. 2012 BD 28.4.2010 Ph.D. UTIA AV CR Pod Vodarenskou vezi 4, 182 08 Praha 8 2010 http://hdl.handle.net/11104/0196899 2010 2010 Praha MFF UK