Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

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.
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Communication dans un congrès
Springer-Verlag Berlin. Second International Conference on Geometric Science of Information, Oct 2015, Palaiseau, France. LNCS 9389, pp.396-405, Proc. of GSI'2015 (Second International Conference on Geometric Science of Information). <10.1007/978-3-319-25040-3_43>
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Dernière modification le : mardi 12 septembre 2017 - 11:41:19
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Jesus Angulo. Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation. Springer-Verlag Berlin. Second International Conference on Geometric Science of Information, Oct 2015, Palaiseau, France. LNCS 9389, pp.396-405, Proc. of GSI'2015 (Second International Conference on Geometric Science of Information). <10.1007/978-3-319-25040-3_43>. <hal-01134047v2>

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