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2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
Color Subspaces as Photometric Invariants
New York, NY
June 17-June 22
ISBN: 0-7695-2597-0
Todd Zickler, Harvard University
Satya P. Mallick, University of California, San Diego
David J. Kriegman, University of California, San Diego
Peter N. Belhumeur, Columbia University
Complex reflectance phenomena such as specular reflections confound many vision problems since they produce image ?features? that do not correspond directly to intrinsic surface properties such as shape and spectral reflectance. A common approach to mitigate these effects is to explore functions of an image that are invariant to these photometric events. In this paper we describe two such invariants" one invariant to specular reflections, and the other invariant to both specular reflections and diffuse shading" that result from exploiting color information in images of dichromatic surfaces. These invariants are derived from subspaces of RGB color space, and they enable the application of Lambertian-based vision techniques to a broad class of specular, non-Lambertian scenes. Using implementations of recent algorithms taken from the literature, we demonstrate the practical utility of these invariants for a wide variety of applications, including stereo, shape from shading, material-based segmentation, and motion estimation.
Citation:
Todd Zickler, Satya P. Mallick, David J. Kriegman, Peter N. Belhumeur, "Color Subspaces as Photometric Invariants," cvpr, vol. 2, pp.2000-2010, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006
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