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Multispectral texture segmentation
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SYSNO ASEP 0411250 Document Type K - Proceedings Paper (Czech conf.) R&D Document Type The record was not marked in the RIV Title Multispectral texture segmentation Author(s) Mikeš, Stanislav (UTIA-B) RID
Haindl, Michal (UTIA-B) RID, ORCIDIssue data Praha: MFF UK, 2003 ISBN 80-86732-18-5 Source Title WDS '03 Proceedings of Contributed Papers / Šafránková J.
s. 221-225Number of pages 5 s. Action Week of Doctoral Students 2003. WDS'03 Event date 10.06.2003-13.06.2003 VEvent location Praha Country CZ - Czech Republic Event type CST Language eng - English Country CZ - Czech Republic Keywords texture ; unsupervised segmentation ; Markov random fields Subject RIV BD - Theory of Information R&D Projects IAA2075302 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) CEZ AV0Z1075907 - UTIA-B Annotation An efficient and robust type of unsupervised multispectral texture segmentation method is presented. The algorithm starts with spectral factorization of an input multispectral texture image using the Karhunen-Loeve expansion. Monospectral factors of single texture patches are assumed to be modelled using a Gaussian Markov random field model. The texture segmentation is done by K-means algorithm in the Markov model parameter space evaluated for each pixel centered image window. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
Number of the records: 1