Kishore Bubna

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Most stereo techniques compute disparity assuming that it varies slowly along surfaces. We quantify and justify this assumption, using weak assumptions about surface orientation distributions in the world to derive the density of disparity surface orientations. The small disparity change assumption is justified by the orientation density's heavy bias toward(More)
1 The authors would like to thank James Miller for helpful discussions and comments. They would also like to acknowledge the financial support of the National Science Foundation under grant IRI-9408700. Abstract The problem of model selection — automatically choosing the correct function to describe a data set — is relevant to many areas of computer vision.(More)
Many problems in computer vision require estimation of both model parameters and boundaries, which limits the usefulness of standard estimation techniques from statistics. Example problems include surface reconstruction from range data, estimation of parametric motion models, fitting circular or elliptic arcs to edgel data, and many others. This paper(More)
This paper introduces a new robust technique that simultaneously estimates model parameters and the domain over which the model applies. It removes the implicit assumption of an innnite domain in standard estimation techniques, and is useful in several computer vision problems. Examples include curve tting, image segmentation, and estimating image-to-image(More)
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