Model-based region growing segmentation of textured images

An approach to the use of a region-growing technique for segmentation of textured images is presented. The algorithm is model-based, with each mixture region in the image modeled by a noncausal Gaussian Markov random field (GMRF). No a priori knowledge about the different texture regions, their asso...

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Bibliographic Details
Published inInternational Conference on Acoustics, Speech, and Signal Processing pp. 2313 - 2316 vol.4
Main Authors Fung, P.W., Grebbin, G., Attikiouzel, Y.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1990
Subjects
Image reconstruction
Image segmentation
Markov random fields
Partitioning algorithms
Robustness
Statistical analysis
Testing
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Summary:An approach to the use of a region-growing technique for segmentation of textured images is presented. The algorithm is model-based, with each mixture region in the image modeled by a noncausal Gaussian Markov random field (GMRF). No a priori knowledge about the different texture regions, their associated texture parameters, or the available number of texture regions is required. The algorithm first partitions the image into small disjointed square windows. The texture within each window is modeled by a noncausal GMRF. Most of the windows are homogeneous. A hierarchical merge-split region-growing process is then employed to reconstruct most of the homogeneous regions that are presented in the image. The growth of various homogeneous regions is directed by a texture distance defined by a likelihood ratio test statistic based on the underlying GMRF model assumptions. The algorithm was tested on real textured images and proved to be robust and effective.< >
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1990.116042