Share Email Print

Proceedings Paper

Topology and parameter estimation in Markov random field modeling
Author(s): Xavier Descombes; Francoise J. Preteux
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Within the framework of pattern recognition via Markov random field modelling, we propose three methods for estimating the topological and statistical parameters characterizing the model, namely clique orders, anisotropy indices, weighting coefficient between cliques with various orders, coefficients of polynomial potential functions and temperature. The developed approaches successively exploit local information associated with conditional probability distributions, a similarity criterion expressed as a distance in variations between appropriate probability distributions, standard least-square estimation and renormalization theory. Extensive experiments performed on a variety of synthetic images have established the relevance and accuracy of the proposed method. Its performances are further demonstrated within the framework of urban areas segmentation in SPOT images.

Paper Details

Date Published: 29 October 1993
PDF: 11 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162034
Show Author Affiliations
Xavier Descombes, Ecole Nationale Superieure des Telecommunications (France)
Francoise J. Preteux, Ecole Nationale Superieure des Telecommunications (France)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

© SPIE. Terms of Use
Back to Top