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Proceedings Paper

Texture-based segmentation using Markov random field models
Author(s): Chi-hsin Wu; Peter C. Doerschuk
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Paper Abstract

We describe segmentation based on textures using the label and image model of D. Geman et al. We replace their maximum a posteriori estimation criteria with a Bayesian estimator that minimizes the sum of the pixel misclassification probabilities. The new estimation goal allows the use of a different computational algorithm based on approximating lattices by trees. An example demonstrating an accurate segmentation of a collage of Brodatz textures is included.

Paper Details

Date Published: 30 June 1994
PDF: 8 pages
Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179216
Show Author Affiliations
Chi-hsin Wu, Purdue Univ (United States)
Peter C. Doerschuk, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 2304:
Neural and Stochastic Methods in Image and Signal Processing III
Su-Shing Chen, Editor(s)

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