Publication details

Multispectral texture segmentation

Conference Paper (Czech conference)

Mikeš Stanislav, Haindl Michal


serial: WDS '03 Proceedings of Contributed Papers, p. 221-225 , Eds: Šafránková J.

publisher: MFF UK, (Praha 2003)

action: Week of Doctoral Students 2003. WDS'03, (Praha, CZ, 10.06.2003-13.06.2003)

research: CEZ:AV0Z1075907

project(s): IST-2001-34744, Commission EC, IAA2075302, GA AV ČR

keywords: texture, unsupervised segmentation, Markov random fields

abstract (eng):

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.

Cosati: 09K

RIV: BD