Object Cutout from Multiview Images using Level Set of Probabilities

We present a novel method to segment an object from multiview images using levelset method. Our approach takes advantage of the unique property of level set method in the flexibility of objective energy function design and the adaptability to cut boundary with arbitrary topology. We introduce an iterating optimized 3D level set framework for view coherent segmentation and propose three forces in this framework to drive the convergence of level set to the ideal boundary. In between, the point cloud term and the edge term are designed to give an as-good-as-possible boundary indicator for the level set function, while the local color discriminative classifier is iteratively updated with the multiview silhouette and the 3D point cloud to drive the deformation of the zero level set. Extensive experimental results demonstrate that our approach can produce much more accurate edge localization and more coherent segmentation result across views, compared with the state-of the-art methods, even for the case of very challenging foreground topologies and ambiguous foreground-background color distribution.