Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours

Based on recent work on Stochastic Partial Differential Equations (SPDEs), this paper presents a simple and well-founded method to implement the stochastic evolution of a curve. First, we explain why great care should be taken when considering such an evolution in a Level Set framework. To guarantee the well-posedness of the evolution and to make it independent of the implicit representation of the initial curve, a Stratonovich differential has to be introduced. To implement this differential, a standard Ito plus drift approximation is proposed to turn an implicit scheme into an explicit one. Subsequently, we consider shape optimization techniques, which are a common framework to address various applications in Computer Vision, like segmentation, tracking, stereo vision etc. The objective

• Type: Journal
• Year: 2006
• Where: IJCV
• Authors: Olivier Juan, Renaud Keriven, Gheorghe Postelnicu
• Keywords: IJCV 2006, Level Set Framework, Shape Optimization Techniques, Stochastic Evolution