1 December 1992 Image segmentation based on composite random field models
Author Affiliations +
Optical Engineering, 31(12), (1992). doi:10.1117/12.60014
Abstract
The problem of region segmentation is examined and a new algorithm for maximum a posteriori (MAP) segmentation is introduced. The observed image is modeled as a composite of two processes: a high-level process that describes the various regions in the images and a low-level process that describes each particular region. A Gibbs-Markov random field model is used to describe the high-level process and a simultaneous autoregressive random field model is used to describe the low-level process. The MAP segmentation algorithm is formulated from the two models and a recursive implementation forthe algorithm is presented. Results of the algorithm on various synthetic and natural textures clearly indicate the effectiveness of the approach to texture segmentation.
Aly A. Farag, Edward J. Delp, "Image segmentation based on composite random field models," Optical Engineering 31(12), (1 December 1992). http://dx.doi.org/10.1117/12.60014
JOURNAL ARTICLE
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KEYWORDS
Image segmentation

Synthetic aperture radar

Image processing

Composites

Image processing algorithms and systems

Volume rendering

Autoregressive models

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