A maximum likelihood approach to segmenting range data

The problem of segmenting a range of image into homogeneous regions, in each of which the range data corresponds to a different surface, is discussed. A maximum-likelihood (ML) segmentation is sought. As most manufactured parts are well approximated by patches of planes, cylinders, and spheres, these three simple surfaces are considered to be the only surfaces in the image. The basic approach to segmentation is to divide the range image into windows, classify each window as a particular surface primitive, and group like windows into surface regions. Grouping windows of the same surface types is cast as a weighted ML clustering problem. Mixed windows are segmented using a ML hierarchical segmentation algorithm. The resulting regions and their associated ML surface parameter and boundary estimates can then be used to perform ML object position estimation and matching. A similar approach is taken for segmenting visible-light images of Lambertian objects illuminated by a point source at infinity.<
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