Surface Reflectance Estimation and Natural Illumination Statistics
Ron O. Dror, Edward H. Adelson, and Alan S. Willsky
Humans recognize optical reflectance properties of surfaces such as metal,
plastic, or paper from a single image without knowledge of illumination.
We develop a machine vision system to perform similar recognition tasks
automatically. Reflectance estimation under unknown, arbitrary
illumination proves highly underconstrained due to the variety of
potential illumination distributions and surface reflectance properties.
We find that the spatial structure of real-world illumination possesses
some of the statistical regularities observed in the natural image
statistics literature. A human or computer vision system may be able to
exploit this prior information to determine the most likely surface
reflectance given an observed image. We develop an algorithm for
reflectance classification under unknown real-world illumination, which
learns relationships between surface reflectance and certain features
(statistics) computed from the observed image. We also develop an
automatic feature selection method.