Nonparametric level-set segmentation based on the minimization of the stochastic complexity
Résumé
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.