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Validation of Watershed Regions by Scale-Space Statistics

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Scale Space and Variational Methods in Computer Vision (SSVM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5567))

Abstract

This paper shows a potential use of scale space for statistical validation of watershed regions of a greyscale image. The watershed segmentation has difficulty in distinguishing valid watershed regions associated with real structures of the image from invalid random regions due to background noise. In this paper, a hierarchy of watershed regions is established by following merging process of the regions in a Gaussian scale space. The distribution of annihilation scales (lives) of the regional minima is investigated to statistically judge the regions as being valid or not. Recursive validation using the hierarchy prevents oversegmentation due to the randomness.

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Author information

Authors and Affiliations

  1. Institute of Media and Information Technology, Chiba University, Japan

    Tomoya Sakai & Atsushi Imiya

Authors
  1. Tomoya Sakai
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  2. Atsushi Imiya
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bergen, Johannes Brunsgate 12, 5008, Bergen, Norway

    Xue-Cheng Tai

  2. Department of Informatics and Centre of Mathematics for Applications, University of Oslo, Oslo, Norway

    Knut Mørken

  3. Simula Research Laboratory, M. Linges v 17, Fornebu, P.O.Box 134, N-1325, Lysaker, Norway

    Marius Lysaker

  4. SINTEF ICT, Oslo, Norway

    Knut-Andreas Lie

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© 2009 Springer-Verlag Berlin Heidelberg

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Sakai, T., Imiya, A. (2009). Validation of Watershed Regions by Scale-Space Statistics. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_15

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  • DOI: https://doi.org/10.1007/978-3-642-02256-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02255-5

  • Online ISBN: 978-3-642-02256-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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