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This œuvre, Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation, by Jesus Angulo is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license.

Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation


Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation
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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.
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Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation
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