Exploiting the generic viewpoint assumption
 William T. Freeman
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The “generic viewpoint” assumption states that an observer is not in a special position relative to the scene. It is commonly used to disqualify scene interpretations that assume special viewpoints, following a binary decision that the viewpoint was either generic or accidental. In this paper, we apply Bayesian statistics to quantify the probability of a view, and so derive a useful tool to estimate scene parameters.
Generic variables can include viewpoint, object orientation, and lighting position. By considering the image as a (differentiable) function of these variables, we derive the probability that a set of scene parameters created a given image. This scene probability equation has three terms: the fidelity of the scene interpretation to the image data; the prior probability of the scene interpretation; and a new genericity term, which favors scenes likely to produce the observed image. The genericity term favors image interpretations for which the image is stable with respect to changes in the generic variables. It results from integration over the generic variables, using a lownoise approximation common in Bayesian statistics.
This approach may increase the scope and accuracy of scene estimates. It applies to a range of vision problems. We show shape from shading examples, where we rank shapes or reflectance functions in cases where these are otherwise unknown. The rankings agree with the perceived values.
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 Title
 Exploiting the generic viewpoint assumption
 Journal

International Journal of Computer Vision
Volume 20, Issue 3 , pp 243261
 Cover Date
 19961201
 DOI
 10.1007/BF00208721
 Print ISSN
 09205691
 Online ISSN
 15731405
 Publisher
 Kluwer Academic Publishers
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