Automatic Selection of Stochastic Watershed Hierarchies

International audienceThe segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set of homogeneous images, our approach selects automatically the best hierarchy and cut level to perform image simplification given an evaluation score. Experimental results illustrate the advantages of our approach on several real-life images datasets

Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

International audienceStochastic watershed is an image segmentation technique based on mathematical morphology which produces a probability density function of image contours. Estimated probabilities depend mainly on local distances between pixels. This paper introduces a variant of stochastic watershed where the probabilities of contours are computed from a Gaussian model of image regions. In this framework, the basic ingredient is the distance between pairs of regions, hence a distance between normal distributions. Hence several alternatives of statistical distances for normal distributions are compared, namely Bhattacharyya distance, Hellinger metric distance and Wasserstein metric distance

Stochastic Watershed Hierarchies

International audienceWe present a segmentation strategy which first constructs a hierarchy, i.e. a series of nested partitions. A coarser partition is obtained by merging adjacent regions in a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. Various strategies are presented for constructing hierarchies which highlight specific features of the image. The core of the paper deals with the stochastic watershed hierarchy. It establishes the probabiity of a contour to appear if a number of random germs are introduced in an image. According to the probability law governing the placement and the shape of the germs, very expressive hierarchies are produced. The last part shows how the hierarchies lead to a final segmentation