MAP segmentation of color images using constraint satisfaction neural network

An improved segmentation algorithm is proposed, which implements the MAP estimation of the label field using a Constraint Satisfaction Neural Network (CSNN). It uses the advantages of stochastic relaxation with those of Gauss-Markov Random Field (GMRF) models. The performance of the algorithm is compared vis-a-vis alternate relaxation schemes using both synthetic and real images.