Bibliografie

(eng): 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.