Share Email Print

Optical Engineering

Image segmentation based on composite random field models
Author(s): Aly A. Farag; Edward J. Delp
Format Member Price Non-Member Price
PDF $20.00 $25.00

Paper 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.

Paper Details

Date Published: 1 December 1992
PDF: 14 pages
Opt. Eng. 31(12) doi: 10.1117/12.60014
Published in: Optical Engineering Volume 31, Issue 12
Show Author Affiliations
Aly A. Farag, Univ. of Louisville (United States)
Edward J. Delp, Purdue Univ. (United States)

© SPIE. Terms of Use
Back to Top