Stability of some segmentation methods based on markov random fields for analysis of aero and space images

Abstract

The paper is devoted to the stability of image segmentation methods based on Markov random fields for analysis of aero and space image with a Gaussian noise and blur. Segmentation problem is formulated in terms of finding a Bayes labeling of an Markov random field with maximum of a posteriori probability by the method of "simulated annealing". We study stability of variants of the algorithm using the Metropolis and Gibbs sampling, the system of neighborhoods with 8 and 24 neighbors and various coefficients of temperature reduction. © 2014 Evgeny Pavlyuk.