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International Conference on Advanced Concepts for Intelligent Vision Systems

ACIVS 2008: Advanced Concepts for Intelligent Vision Systems pp 506–517Cite as

Nonparametric Level-Set Segmentation Based on the Minimization of the Stochastic Complexity

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5259))

Abstract

In this paper, a novel non parametric method of image segmentation is deduced from the stochastic complexity principle. The main advantage of this approach is that it does not rely on any assumption on the probability density functions in each region and does not include any free parameter that has to be adjusted by the user in the optimized criterion. This results in a very flexible and robust segmentation algorithm. Various simulations performed with both synthetic and real images show that the proposed non parametric algorithm performs similarly to the parametric counterparts with the flexibility of a nonparametric approach.

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Author information

Authors and Affiliations

  1. The authors are with the Physics and Image Processing Group, Institut Fresnel, CNRS, Aix-Marseille Université; Ecole Centrale de Marseille, Campus de Saint Jérôme, 13397, Marseille Cedex 20

    Marc Allain, Nicolas Bertaux & Frederic Galland

Authors
  1. Marc Allain
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  2. Nicolas Bertaux
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  3. Frederic Galland
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Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Ecole Centrale de Marseille, 13451, Marseille, France

    Salah Bourennane

  3. Ghent University, 9000, Gent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, NSW 1710, Sydney, Australia

    Dan Popescu

  5. University of Antwerp, 2610, Wilrijk, Belgium

    Paul Scheunders

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© 2008 Springer-Verlag Berlin Heidelberg

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Allain, M., Bertaux, N., Galland, F. (2008). Nonparametric Level-Set Segmentation Based on the Minimization of the Stochastic Complexity. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_46

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  • DOI: https://doi.org/10.1007/978-3-540-88458-3_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88457-6

  • Online ISBN: 978-3-540-88458-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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