Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation  Jesús Angulo

Author: Jesús Angulo
DOI URL: http://dx.doi.org/10.1007/9783319250403_43
Video: http://www.youtube.com/watch?v=L96rEpsPm4s
Slides: https://drive.google.com/open?id=0B0QKxsVtOaTiNklpWThBRHBBRVk
Presentation: https://www.see.asso.fr/node/14305
Creative Commons AttributionShareAlike 4.0 InternationalAbstract:
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.