Number of the records: 1  

Image Segmentation

  1. 1.
    0359021 - ÚTIA 2012 CZ eng D - Thesis
    Mikeš, Stanislav
    Image Segmentation.
    UTIA AV CR. Defended: MFF UK Praha. 28.4.2010. - Praha: MFF UK, 2010. 196 s.
    R&D Projects: GA MŠMT 1M0572; GA ČR GA102/08/0593
    EU Projects: European Commission(XE) 507752 - MUSCLE
    Grant - others:GA MŠk(CZ) 2C06019
    Institutional research plan: CEZ:AV0Z10750506
    Keywords : iamge segmentation * Markov random fields
    Subject RIV: BD - Theory of Information

    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.
    Permanent Link: http://hdl.handle.net/11104/0196899

     
     
Number of the records: 1  

  This site uses cookies to make them easier to browse. Learn more about how we use cookies.