Segmentation of noisy textured images using simulated annealing

This paper presents a segmentation algorithm for noisy textured images. To represent noisy textured images, we propose a hierarchical stochastic model that consists of three levels of random fields: the region process, the texture processes and the noise. The hierarchical model also includes local blurring and nonlinear image transformation as results of the image corrupting effects. Having adopted a statistical model, the maximum a posteriori (MAP) estimation is used to find the segmented regions through the restored(noise-free) textured image data. Since the joint a posteriori distribution at hand is a Gibbs distribution, we use simulated annealing as a maximization technique. The simulated annealing based segmentation algorithm presented in this paper can also be viewed as a two-step iterative algorithm in the spirit of the EM algorithm [10].