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International Conference on Geometric Science of Information

GSI 2015: Geometric Science of Information pp 396–405Cite as

Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9389))

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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Author information

Authors and Affiliations

  1. MINES ParisTech, CMM-Centre de Morphologie Mathématique, PSL-Research University, Paris, France

    Jesús Angulo

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  1. Jesús Angulo
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Correspondence to Jesús Angulo .

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Editors and Affiliations

  1. Bâtiment Alan Turing, CS35003, École Polytechnique, Palaiseau, France

    Frank Nielsen

  2. Thales Land\& Air Systems, Limours, France

    Frédéric Barbaresco

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Angulo, J. (2015). Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2015. Lecture Notes in Computer Science(), vol 9389. Springer, Cham. https://doi.org/10.1007/978-3-319-25040-3_43

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  • DOI: https://doi.org/10.1007/978-3-319-25040-3_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25039-7

  • Online ISBN: 978-3-319-25040-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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