TR#163: Cooperative Robust Estimation Using Layers of Support

Trevor J. Darrell and Alex P. Pentland

We present an approach to the problem of representing images that contain multiple objects or surfaces. Rather than use an edge-based approach to represent the segmentation of a scene, we propose a multi-layer 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 which provide a theoretical basis for support-based estimation. The Minimum Description Length principle is used to decide how many support maps to use in describing a particular image. We show results applying this framework to heterogeneous interpolation and segmentation tasks on range and motion imagery.