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Abstract:
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.
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