Cooperative robust estimation using layers of support

We present an approach to the problem of representing images that contain multiple objects or surfaces. Rather than using an edge-based approach to represent the segmentation of a scene, we propose a multilayer estimation framework which uses support maps to represent the segmentation of the image into homogeneous chunks. This support-based approach can represent objects that are split into disjoint regions, or have surfaces that are transparently interleaved. Our framework is based on an extension of robust estimation methods that provide a theoretical basis for support-based estimation. We use a selection criteria derived from the minimum description length principle to decide how many support maps to use in describing an image. Our method has been applied to a number of different domains, including the decomposition of range images into constituent objects, the segmentation of image sequences into homogeneous higher-order motion fields, and the separation of tracked motion features into distinct rigid-body motions