Model based region segmentation using cooccurrence matrices

A region segmentation algorithm is presented, using a model for joint probability density. Joint probability density can be defined as an N×N cooccurrence matrix in which each coordinate (i, j) gives the probability for the gray-level transition i, j between two neighbor pixels. The approach consists in modeling the energy distribution within a cooccurrence matrix of a region. Regions are assumed to be stationary. A region-growing scheme that proceeds in two steps is used. The first step consists of learning the parameters of the model. The second step is the segmentation process. Starting with a seed pixel, new pixels are incorporated in the region if their neighborhoods fit the model