Image Statistics and Surface Perception

Tuesday, March 7, 2006 - 1:30pm - 2:30pm
EE/CS 3-180
Edward Adelson (Massachusetts Institute of Technology)
It is mathematically impossible to tell whether a surface is white, gray, or
black, by looking at it in isolation, since the luminance is the product of
two unknown variables, illumination and reflectance (albedo). Nonetheless
people can do it pretty well, proving that the human visual system is
smarter than the people who study it. Real surfaces, such as paper, cloth,
or stucco, have visual textures that depend on interreflections and specular
reflections, and some of the resultant image statistics are correlated with
surface properties such as albedo and gloss. By manipulating these
statistics, we can make the surface look lighter or darker (and duller or
shinier) without changing the mean luminance. In a related project, we are
exploring how local statistics can be used to separate shading and albedo
in natural images. Working in the derivative domain (as in Retinex), we
train on images with ground truth intrinsic images of shading and albedo,
and learn to estimate the derivatives based on local image patches. We then
do a pseudoinverse to retrieve the images. The results are good: we can
separate an image into its shading and albedo components better than
previous methods, including our own previous methods that relied on
classification rather than estimation.