Variational Segmentation using Fuzzy Region Competition and Local Non-Parametric Probability Density Functions

We describe a novel variational segmentation algorithm designed to split an image in two regions based on their intensity distributions. A functional is proposed to integrate the unknown probability density functions of both regions within the optimization process. The method simultaneously performs segmentation and non-parametric density estimation. It does not make any assumption on the underlying distributions, hence it is flexible and can be applied to a wide range of applications. Although a boundary evolution scheme may be used to minimize the functional, we choose to consider an alternative formulation with a membership function. The latter has the advantage of being convex in each variable, so that the minimization is faster and less sensitive to initial conditions. Finally, to improve the accuracy and the robustness to low-frequency artifacts, we present an extension for the more general case of local space-varying probability densities. The approach readily extends to vector...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Benoit Mory, Roberto Ardon, Jean-Philippe Thiran
• Keywords: Boundary Evolution Scheme, Computer Vision, ICCV 2007, Non-parametric Density Estimation, Space-varying Probability Densities, Unknown Probability Density, Variational Segmentation Algorithm