Institute of Information Theory and Automation

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Publication details

Visual Data Recognition and Modeling Based on Local Markovian Models

Monography Chapter

Haindl Michal

serial: Mathematical Methods for Signal and Image Analysis and Representation, p. 241-259 , Eds: Florack Luc, Duits Remco, Jongbloed Geurt, Lieshout Marie-Colette, Davies Laurie

research: CEZ:AV0Z10750506

project(s): 1M0572, GA MŠk, 387/2010, CESNET, GAP103/11/0335, GA ČR, GA102/08/0593, GA ČR

keywords: Markov random fields, image modeling, image recognition

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abstract (eng):

An exceptional 3D wide-sense Markov model which can be completely solved analytically and easily synthesised is presented. The model can be modified to faithfully represent complex local data by adaptive numerically robust recursive estimators of all its statistics. Illumination invariants can be derived from some of its recursive statistics and exploited in content based image retrieval, supervised or unsupervised image recognition. Its modelling efficiency is demonstrated on several analytical and modelling image applications, in particular on unsupervised image or range data segmentation, bidirectional texture function (BTF) synthesis and compression, dynamic texture synthesis and adaptive multispectral and multichannel image and video restoration.


2012-12-21 16:10