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NIPS
1997

Bayesian Model of Surface Perception

10 years 5 months ago
Bayesian Model of Surface Perception
Image intensity variations can result from several different object surface effects, including shading from 3-dimensional relief of the object, or paint on the surface itself. An essential problem in vision, which people solve naturally, is to attribute the proper physical cause, e.g. surface relief or paint, to an observed image. We addressed this problem with an approach combining psychophysical and Bayesian computational methods. We assessed human performance on a set of test images, and found that people made fairly consistent judgements of surface properties. Our computational model assigned simple prior probabilities to different relief or paint explanations for an image, and solved for the most probable interpretation in a Bayesian framework. The ratings of the test images by our algorithm compared surprisingly well with the mean ratings of our subjects. Neural Information Processing Systems, Vol. 10, 1998. This work may not be copied or reproduced in whole or in part for any c...
William T. Freeman, Paul A. Viola
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1997
Where NIPS
Authors William T. Freeman, Paul A. Viola
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