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Image Segmentation
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SYSNO ASEP 0359021 Document Type D - Thesis R&D Document Type The record was not marked in the RIV Title Image Segmentation Author(s) Mikeš, Stanislav (UTIA-B) RID Issue data Praha: MFF UK, 2010 Number of pages 196 s. Language eng - English Country CZ - Czech Republic Keywords iamge segmentation ; Markov random fields Subject RIV BD - Theory of Information R&D Projects 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) GA102/08/0593 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
Number of the records: 1