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Authors: P. Martin 1 ; Ph. Réfrégier 2 ; F. Galland 2 and F. Guérault 3

Affiliations: 1 Physics and Image Processing group, Fresnel Institute, Université Paul Césanne Aix-Marseille III; Simag Développement, France ; 2 Physics and Image Processing group, Fresnel Institute, Université Paul Césanne Aix-Marseille III, France ; 3 Simag Développement, France

Keyword(s): Segmentation, Level-set, Minimum Description Length principle, Statistical Estimation.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: In this paper, we focus on the segmentation of objects not necessarily simply connected using level set snakes and we present a nonparametric statistical approach based on the minimization of the stochastic complexity (Minimum Description Length principle). This approach allows one to get a criterion to optimize with no free parameter to be tuned by the user. We thus propose to estimate the probability law of the gray levels of the object and the background of the image with a step function whose order is automatically determinated. We show that coupling the probability law estimation and the segmentation steps leads to good results on various types of images. We illustrate the robustness of the proposed nonparametric statistical snake on different examples and we show on synthetic images that the segmentation results are equivalent to those obtained with a parametric statistical technique, although the technique is non parametric and without ad hoc parameter in the optimized criteri on. (More)

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Paper citation in several formats:
Martin, P.; Réfrégier, P.; Galland, F. and Guérault, F. (2006). NONPARAMETRIC STATISTICAL LEVEL SET SNAKE BASED ON THE MINIMIZATION OF THE STOCHASTIC COMPLEXITY. In Proceedings of the First International Conference on Computer Vision Theory and Applications (VISIGRAPP 2006) - Volume 1: VISAPP; ISBN 972-8865-40-6; ISSN 2184-4321, SciTePress, pages 463-467. DOI: 10.5220/0001363204630467

@conference{visapp06,
author={P. Martin. and Ph. Réfrégier. and F. Galland. and F. Guérault.},
title={NONPARAMETRIC STATISTICAL LEVEL SET SNAKE BASED ON THE MINIMIZATION OF THE STOCHASTIC COMPLEXITY},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications (VISIGRAPP 2006) - Volume 1: VISAPP},
year={2006},
pages={463-467},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001363204630467},
isbn={972-8865-40-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the First International Conference on Computer Vision Theory and Applications (VISIGRAPP 2006) - Volume 1: VISAPP
TI - NONPARAMETRIC STATISTICAL LEVEL SET SNAKE BASED ON THE MINIMIZATION OF THE STOCHASTIC COMPLEXITY
SN - 972-8865-40-6
IS - 2184-4321
AU - Martin, P.
AU - Réfrégier, P.
AU - Galland, F.
AU - Guérault, F.
PY - 2006
SP - 463
EP - 467
DO - 10.5220/0001363204630467
PB - SciTePress