SEGMENTATION OF NOISY TEXTURED IMAGES USING SIMULATED ANNEALING.

The authors present a segmentation algorithm for noisy textured images. To represent noisy textured images, they propose a hierarchical stochastic model that consists of three levels of random fields: the region process, the texture process 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, simulated annealing is used as a maximization technique. The simulated-annealing-based segmentation algorithm presented can also be viewed as a two-step iterative algorithm in the spirit of the EM algorithm (see Dempster, A. P. et. al. , J. Royal Stat. Society, vol. 39, pp. 1-38, 1977).