Bayesian decision feedback for segmentation of binary images

We present real-time algorithms for the segmentation of binary images modeled by Markov mesh random fields (MMRFs) and corrupted by independent noise. The goal is to find a recursive algorithm to compute the MAP estimate of each pixel of the scene using a fixed lookahead of D rows and D columns of the observations. The optimal algorithm for this is computationally expensive. Using both hard and soft (conditional) decision feedback, the complexity is reduced in a principled manner to allow a performance/complexity tradeoff. Simulation results demonstrate the viability of the algorithm and it´s subjective relevance to the image segmentation problem