Region Extraction Based on Belief Propagation for Gaussian Model

We show a fast algorithm for region extraction based on belief propagation with loopy networks. The solution to this region segmentation problem, which includes the region extraction problem, is of significant computational cost if a conventional iterative approach or statistical sampling methods are applied. In the proposed approach, Gaussian loopy belief propagation is applied to a continuous-valued problem that replaces the discrete labeling problem. We show that the computational cost for region extraction can be reduced by using this algorithm, and apply the method to the extraction of a discontinuous area in Moiré topography.