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Proceedings Paper

MRF-based texture segmentation using wavelet decomposed images
Author(s): Hideki Noda; Mahdad N. Shirazi; Eiji Kawaguchi
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Paper Abstract

One difficulty of textured image segmentation in the past was the lack of computationally efficient models which can capture the statistical regularities of textures over large distances. Recently, to overcome this difficulty, Bayesian approaches capitalizing on the computational efficiency of multiresolution representations have received attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid decomposition as the image decomposition scheme. In this paper, motivated by the 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 the image. The model, using doubly stochastic Markov random fields (MRFs), captures intrascale statistical dependencies over the observed image's wavelet decomposition and intrascale and interscale statistical dependencies over the corresponding multiresolution region image (an unobserved image which contains the classification of pixels in the image). For the sake of computational efficiency, versions of the Expectation-Maximization (EM) algorithm and Maximum a posteriori (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.

Paper Details

Date Published: 19 April 2000
PDF: 11 pages
Proc. SPIE 3974, Image and Video Communications and Processing 2000, (19 April 2000); doi: 10.1117/12.383009
Show Author Affiliations
Hideki Noda, Kyushu Institute of Technology (Japan)
Mahdad N. Shirazi, Communications Research Lab. (Japan)
Eiji Kawaguchi, Kyushu Institute of Technology (Japan)

Published in SPIE Proceedings Vol. 3974:
Image and Video Communications and Processing 2000
Bhaskaran Vasudev; T. Russell Hsing; Andrew G. Tescher; Robert L. Stevenson, Editor(s)

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