Presentation 2002/1/11
UNSUPERVISED TEXTURE SEGMENTATION BASED ON MULTISCALE STOCHASTIC MODELING IN WAVELET DOMAIN
Hideki Noda, N. Shirazi Mahdad, Eiji Kawaguchi,
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Abstract(in English) One difficulty of textured image segmentation in the past was the lack of computationally efficient models which can capture statistical regularities of textures over large distances. Recently, to overcome this difficulty, Bayesian approaches capitalizing on computational efficiency of multiscale representations have received attention. Most of previous researches have been based on multiscale stochastic models which use the Gaussian pyramid decomposition as image decomposition scheme. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm which is based on a multiscale stochastic modeling over the wavelet decomposition of image. For the sake of computational efficiency, versions of the EM algorithm and MAP estimate, which are based on the mean-field decomposition of a posteriori probability, are used for estimating model parameters and the segmented image, respectively.
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Paper # 2001-IE-169
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Committee IE
Conference Date 2002/1/11(1days)
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Title (in Japanese) (See Japanese page)
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Title (in English) UNSUPERVISED TEXTURE SEGMENTATION BASED ON MULTISCALE STOCHASTIC MODELING IN WAVELET DOMAIN
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1st Author's Name Hideki Noda
1st Author's Affiliation Kyushu Institute of Technology()
2nd Author's Name N. Shirazi Mahdad
2nd Author's Affiliation Communications Research Laboratory
3rd Author's Name Eiji Kawaguchi
3rd Author's Affiliation Kyushu Institute of Technology
Date 2002/1/11
Paper # 2001-IE-169
Volume (vol) vol.101
Number (no) 567
Page pp.pp.-
#Pages 6
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