Image segmentation based on composite random field models

Aly A. Farag; Edward J. Delp

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.

Citation Download Citation

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

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