Coding-oriented segmentation based on Gibbs-Markov random fields and human visual system knowledge

A new segmentation algorithm for still black and white images is introduced. This algorithm forms the basis of a region-oriented sequence coding technique, currently under development. The algorithm models the human mechanism of selecting regions both by their interior characteristics and their boundaries. This is carried out in two different stages: with a preprocessing that takes into account only gray level information, and with a stochastic model for segmented images that uses both region interior and boundary information. In the stochastic model, the gray level information within the regions is modeled by stationary Gaussian processes, and the boundary information by a Gibbs-Markov random field (GMRF). The segmentation is carried out by finding the most likely realization of the joint process (maximum a posteriori criterion), given the preprocessed image. For decreasing the computational load while avoiding local maxima in the probability function, suboptimal versions of the algorithm are proposed