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Summary: Recognition of Surface Reflectance Properties from a Single Image under
Unknown Real-World Illumination
Ron O. Dror, Edward H. Adelson, and Alan S. Willsky
Massachusetts Institute of Technology
rondror@ai.mit.edu, adelson@psyche.mit.edu, willsky@mit.edu
Published in: Proceedings of the IEEE Workshop on Identifying Objects Across Variations in
Lighting: Psychophysics & Computation. Colocated with CVPR 2001.
Kauai, Hawaii, December 2001. c IEEE Computer Society.
Abstract
This paper describes a machine vision system that clas-
sifies reflectance properties of surfaces such as metal, plas-
tic, or paper, under unknown real-world illumination. We
demonstrate performance of our algorithm for surfaces of
arbitrary geometry. Reflectance estimation under arbi-
trary omnidirectional illumination proves highly undercon-
strained. Our reflectance estimation algorithm succeeds by
learning relationships between surface reflectance and cer-
tain statistics computed from an observed image, which de-
pend on statistical regularities in the spatial structure of
real-world illumination. Although the algorithm assumes
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