Surface Reflectance Estimation and Natural Illumination Statistics
DSpace@MIT (Massachusetts Institute of Technology)Published 1 September 2001Open access
Ron O. Dror, Edward H. Adelson, Alan S. Willsky
Citations20
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TL;DR
An algorithm for reflectance classification under unknown real-world illumination is developed, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image, and an automatic feature selection method is developed.
Abstract
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 have found 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 a single observed image. We also develop an automatic feature selection method.
Keywords
Computer ScienceEngineeringPhysics and Astronomy
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