Statistical Gaussian Model of Image Regions in Stochastic Watershed SegmentationReport as inadecuate




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1 CMM - Centre de Morphologie Mathématique

Abstract : Stochastic 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.





Author: Jesus Angulo -

Source: https://hal.archives-ouvertes.fr/



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