In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the minimal cost criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph (NNG) in each iteration.

DRM\fleurshinagawa.jpg
...\Main.m
...\srm.m
...\srm_boundarygradient.c
...\srm_boundarygradient.mexw32
...\srm_boundarylen.c
...\srm_boundarylen.mexw32
...\srm_demo.m
...\srm_getborders.m
...\srm_imgGrad.m
...\srm_plot_segmentation.m
...\srm_randimseg.m
...\vl_tightsubplot.m
DRM

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