The use of Gibbs random fields for image segmentation

Presents a robust and adaptive technique for segmentation of a noisy image. The original image is modeled by an underlying Gibbs random field, and the noise is the mixture of an additive independent Gaussian noise and a salt or pepper noise. The processes of maximum a posteriori segmentation and maximum-likelihood estimation for the image model parameters are carried out simultaneously