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Stochastic Mean Curvature Motion in Computer Vision : Stochastic Active Contours

This paper presents a novel framework for image segmentation based on stochastic optimization. During the last few years, several segmentation methods have been proposed to integrate different information in a variational framework, where an objective function depending on both boundary information and region information is minimized using a gradient-descent method. Some recent methods are even able to extract the region model during the segmentation process itself. Yet, in complex cases, the objective function does not have any computable gradient. In other cases, the minimization process gets stuck in some local minimum, while no multi-resolution approach can be invoked. To deal with those two frequent problems, we propose a stochastic optimization approach and show that even a simple Simulated Annealing method is powerful enough in many cases. Based on recent work on Stochastic Partial Differential Equations (SPDEs), we propose a simple and well-founded method to implement the stochastic evolution of a curve in a Level Set framework. The performance of our method is demonstrated on both synthetic and real images.