LAMBERTIAN REFLECTANCE AND LINEAR SUBSPACES PDF

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: ICCV We prove that the set of all reflectance functions the mapping from surface normals to intensities produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that the images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately with a low-dimensional linear subspace, explaining prior empirical results.

Author:Tojakinos Akishakar
Country:Norway
Language:English (Spanish)
Genre:Automotive
Published (Last):26 June 2006
Pages:417
PDF File Size:10.51 Mb
ePub File Size:8.14 Mb
ISBN:885-6-56302-390-5
Downloads:83088
Price:Free* [*Free Regsitration Required]
Uploader:Gugul



Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: ICCV We prove that the set of all reflectance functions the mapping from surface normals to intensities produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that the images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately with a low-dimensional linear subspace, explaining prior empirical results.

We also provide a simple analytic characterization of this linear space. View on IEEE. Save to Library. Create Alert. Launch Research Feed. Share This Paper. Figures, Tables, and Topics from this paper. Figures and Tables. Citations Publications citing this paper.

Gortler , Todd E. Zickler Towards accurate and efficient representation of image irradiance of convex-Lambertian objects under unknown near lighting Shireen Y. Elhabian , Ham M. Rara , Aly A. Nguyen , Minh N. Non-negative lighting and specular object recognition Sameer Shirdhonkar , David W. Shape estimation in natural illumination Micah K. Johnson , Edward H. References Publications referenced by this paper.

Pattern Anal. Koenderink , Andrea J. Photometric stereo under a light source with arbitrary motion Hideki Hayakawa Physics Predicting reflectance functions from complex surfaces Stephen H. Westin , James Arvo , Kenneth E. Bidirectional reflection functions from surface bump maps Brian Cabral , Nelson L. Nimeroff , Eero P. A low-dimensional representation of human faces for arbitrary lighting conditions Peter W. Related Papers. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy , Terms of Service , and Dataset License.

HATCO HDW-1 PDF

Lambertian reflectance and linear subspaces

The present invention relates generally to computer vision and, more particularly, to image recognition and model reconstructions systems. One of the most basic problems in vision is to understand how variability in lighting affects the images that an object can produce. Even when lights are isotropic and relatively far from an object, it has been shown that smooth Lambertian objects can produce infinite-dimensional sets of images. It has been very popular in object recognition to represent the set of images that an object can produce using low dimensional linear subspaces of the space of all images. There are those in the art who have analytically derived such a representation for sets of 3D points undergoing scaled orthographic projection. Still others have derived a 3D linear representation of the set of images produced by a Lambertian object as lighting changes, though this simplified representation assigns negative intensities in places where the surface normals are facing away from the light.

GEORGE SMEATON ATONEMENT PDF

US6853745B1 - Lambertian reflectance and linear subspaces - Google Patents

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In.

JB ARCHIBALD MACLEISH PDF

登录 the Lens

Abstract—We prove that the set of all Lambertian reflectance functions the mapping from surface normals to intensities obtained with arbitrary distant light sources lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace, explaining prior empirical results. We also provide a simple analytic characterization of this linear space. We obtain these results by representing lighting using spherical harmonics and describing the effects of Lambertian materials as the analog of a convolution. These results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce nonnegative lighting functions. We also show a simple way to enforce nonnegative lighting when the images of an object lie near a 4D linear space. We apply these algorithms to perform face recognition by finding the 3D model that best matches a 2D query image.

Related Articles