Abstract
Recently a new approach to Bayesian image segmentation has been propesed by Bouman and Shapiro [1], based on a multiscale random field (MSRF) model along with a sequential MAP (SMAP) estimator as an efficient and computationally feasible alternative to MAP segmentation. But their method is restricted to image models with observed pixels that are conditionally independent given their class labels. In this paper, we follow the approach of [1] and extend the SMAP method for a more general class of random field models. The proposed scheme is recursive, yields the exact MAP estimate, and is readily applicable to a broad range of image models. We present simulations on synthetic images and conclude that the generalized algorithm performs better and requires much less computation than maximum likelihood segmentation.
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