Image segmentation based on composite random field models

Aly A. Farag; Edward J. Delp III

doi:10.1117/12.60014

1 December 1992 Image segmentation based on composite random field models

Aly A. Farag, Edward J. Delp III

Author Affiliations +

Optical Engineering, Vol. 31, Issue 12, (December 1992). https://doi.org/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.

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Aly A. Farag and Edward J. Delp III "Image segmentation based on composite random field models," Optical Engineering 31(12), (1 December 1992). https://doi.org/10.1117/12.60014

Published: 1 December 1992

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