Y. Tsin, R. Collins, V. Ramesh, and T. Kanade,
"Bayesian Color Constancy for Outdoor Object Recognition,"
IEEE Computer Vision and Pattern Recognition,
Kauai, Hawaii, December 2001, pp.1132-1139.
Outdoor scene classification is challenging due to irregular geometry,
uncontrolled illumination, and noisy reflectance distributions. This
paper discusses a Bayesian approach to classifying a color image of an
outdoor scene. A likelihood model factors in the physics of the image
formation process, the sensor noise distribution, and prior
distributions over geometry, material types, and illuminant spectrum
parameters. These prior distributions are learned through a training
process that uses color observations of planar scene patches over
time. An iterative linear algorithm estimates the maximum likelihood
reflectance, spectrum, geometry, and object class labels for a new
image. Experiments on images taken by outdoor surveillance cameras
classify known material types and shadow regions correctly, and flag
as outliers material types that were not seen previously.
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