Nonparametric statistical snake based on the minimum stochastic complexity.
We propose a nonparametric statistical snake technique that is based on the minimization of the stochastic complexity (minimum description length principle). The probability distributions of the gray levels in the different regions of the image are described with step functions with parameters that are estimated. The segmentation is thus obtained by minimizing a criterion that does not include any parameter to be tuned by the user. We illustrate the robustness of this technique on various types of images with level set and polygonal contour models. The efficiency of this approach is also analyzed in comparison with parametric statistical techniques.
Martin
Pascal
P
Physics and Image Processing Group, Fresnel Institute UMR CNRS 6133, Ecole Généraliste d'Ingénieurs de Marseille, Domaine Universitaire de St Jérôme, 13397 Marseille 20, France. pascal.martin@fresnel.fr
Réfrégier
Philippe
P
Galland
Frédéric
F
Guérault
Frédéric
F
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013269:Stochastic Processes