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Estimating Surface Reflectance Properties from Images under Unknown Illumination

Summary: Estimating Surface Reflectance Properties from Images under
Unknown Illumination
Ron O. Drora, Edward H. Adelsonb, and Alan S. Willskya
aDepartment of Electrical Engineering and Computer Science
bDepartment of Brain and Cognitive Sciences
Massachusetts Institute of Technology
Published in: Proceedings of the SPIE 4299: Human Vision and Electronic Imaging IV,
San Jose, California, January 2001. c SPIE
Physical surfaces such as metal, plastic, and paper possess different optical qualities that lead to different character-
istics in images. We have found that humans can effectively estimate certain surface reflectance properties from a
single image without knowledge of illumination. We develop a machine vision system to perform similar reflectance
estimation tasks automatically. The problem of estimating reflectance from single images under unknown, complex
illumination proves highly underconstrained due to the variety of potential reflectances and illuminations. Our so-
lution relies on statistical regularities in the spatial structure of real-world illumination. These regularities translate
into predictable relationships between surface reflectance and certain statistical features of the image. We determine
these relationships using machine learning techniques. Our algorithms do not depend on color or polarization; they
apply even to monochromatic imagery. An ability to estimate reflectance under uncontrolled illumination will further
efforts to recognize materials and surface properties, to capture computer graphics models from photographs, and to
generalize classical motion and stereo algorithms such that they can handle non-Lambertian surfaces.


Source: Adelson, Edward - Computer Science and Artificial Intelligence Laboratory, Department of Brain and Cognitive Science, Massachusetts Institute of Technology (MIT)


Collections: Biology and Medicine