The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Dec. 28, 2010
Filed:
Feb. 12, 2010
Satya P. Mallick, La Jolla, CA (US);
David J. Kriegman, San Diego, CA (US);
Todd E. Zickler, Cambridge, MA (US);
Peter N. Belhumeur, New York, NY (US);
Satya P. Mallick, La Jolla, CA (US);
David J. Kriegman, San Diego, CA (US);
Todd E. Zickler, Cambridge, MA (US);
Peter N. Belhumeur, New York, NY (US);
The Regents of the University of California, Oakland, CA (US);
Abstract
The present invention presents a framework for separating specular and diffuse reflection components in images and videos. Each pixel of the an M-channel input image illuminated by N light sources is linearly transformed into a new color space having (M−N) channels. For an RGB image with one light source, the new color space has two color channels (U,V) that are free of specularities and a third channel (S) that contains both specular and diffuse components. When used with multiple light sources, the transformation may be used to produce a specular invariant image. A diffuse RGB image can be obtained by applying a non-linear partial differential equation to an RGB image to iteratively erode the specular component at each pixel. An optional third dimension of time may be added for processing video images. After the specular and diffuse components are separated, dichromatic editing may be used to independently process the diffuse and the specular components to add or suppress visual effects. The (U,V) channels of images can be used as input to 3-D shape estimation algorithms including shape-from-shading, photometric stereo, binocular and multinocular stereopsis, and structure-from-motion.