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

Authors:
Marc Allain;Nicolas Bertaux;Frederic Galland
Affiliations:
The authors are with the Physics and Image Processing Group, Institut Fresnel, CNRS, Aix-Marseille Université// Ecole Centrale de Marseille, Marseille Cedex 20, 13397;The authors are with the Physics and Image Processing Group, Institut Fresnel, CNRS, Aix-Marseille Université// Ecole Centrale de Marseille, Marseille Cedex 20, 13397;The authors are with the Physics and Image Processing Group, Institut Fresnel, CNRS, Aix-Marseille Université// Ecole Centrale de Marseille, Marseille Cedex 20, 13397
Venue:
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Year:
2008

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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.