Stochastic Complexity based Image Segmentation with unknown Noise Model
Guillaume Delyon, P. Martin, Ph. Réfrégier, Frédéric Guérault, Frédéric Galland · AIP conference proceedings · 2006
We propose a general statistical image segmentation method which does not need any a priori knowledge of the probability density functions (PDF) of the grey levels of the image. This method is based on the minimization of the stochastic complexity (Minimum Description Length principle) which leads to optimize a criterion without parameter to be tuned by the user which is adapted to the PDF of the grey levels of the image. We apply this method to three partition descriptors: a polygonal active contour, a level set implementation and a polygonal active grid. We illustrate the technique on real images.