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Proceedings Paper

Estimating surface reflectance properties from images under unknown illumination
Author(s): Ron O. Dror; Edward H. Adelson; Alan S. Willsky
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Paper Abstract

Physical surfaces such as metal, plastic, and paper possess different optical qualities that lead to different characteristics 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 form single images under unknown, complex illumination proves highly under-constrained due to the variety of potential reflectances and illuminations. Our solution 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, tp capture computer graphics models from photographs, and to generalize classical motion and stereo algorithms such that they can handle non-Lambertian surfaces.

Paper Details

Date Published: 8 June 2001
PDF: 12 pages
Proc. SPIE 4299, Human Vision and Electronic Imaging VI, (8 June 2001); doi: 10.1117/12.429494
Show Author Affiliations
Ron O. Dror, Massachusetts Institute of Technology (United States)
Edward H. Adelson, Massachusetts Institute of Technology (United States)
Alan S. Willsky, Massachusetts Institute of Technology (United States)


Published in SPIE Proceedings Vol. 4299:
Human Vision and Electronic Imaging VI
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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