Abstract

We propose a variational framework for the integration mul- tiple competing shape priors into level set based segmentation schemes. By optimizing an appropriate cost functional with respect to both a level set function and a (vector-valued) labeling function, we jointly generate a segmentation (by the level set function) and a recognition-driven par- tition of the image domain (by the labeling function) which indicates where to enforce certain shape priors. Our framework fundamentally ex- tends previous work on shape priors in level set segmentation by directly addressing the central question of where to apply which prior. It allows for the seamless integration of numerous shape priors such that – while segmenting both multiple known and unknown ob jects – the level set process may selectively use specific shape knowledge for simultaneously enhancing segmentation and recognizing shape.