Kishore Bubna

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The problem of model selection — automatically choosing the correct function to describe a data set — is relevant to many areas of computer vision. Many model selection criteria have been used in the vision literature and many more have been proposed in statistics, but the relative strengths of these criteria have not been analyzed in vision. Using the(More)
The problem of model selection is relevant to many areas of computer vision. Model selection criteria have been used in the vision literature and many more have been proposed in statistics, but the relative strengths of these criteria have not been analyzed in vision. More importantly, suitable extensions to these criteria must be made to solve problems(More)
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)
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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