Paper Info
Title
Noise Reduction In Photometric Stereo With Non-Distant Light Sources
Abstract
In classical photometric stereo, a Lambertian surface is illuminated from multiple distant point light-sources. In the present paper we consider nearby light sources instead, so that the unknown surface, is illuminated by non-parallel beams of light. In continuous noiseless cases, the recovery of a I.,ambertian surface from non-distant illuminations, reduces to solving a system of non-linear partial differential equations for a bivariate function it, whose graph is the visible part of the surface. This system is more difficult to analyse than its counterpart, where light-sources are at infinity. We consider here a similar task, but with slightly more realistic assumptions: the photographic images are discrete and contaminated by Gaussian noise. This leads to a non-quadratic optimization problem involving a large number of independent variables. The latter imposes a heavy computational burden (due to the large matrices involved) for standard optimization schemes. We test here a feasible alternative: an iterative scheme called 2-dimensional Leap-Frog Algorithm(14). For this we describe an implementation for three light-sources in sufficient detail to permit code to be written. Then we give examples verifying experimentally the performance of Leap-Frog.
Year
DOI
Venue
2004
10.1007/1-4020-4179-9_16
COMPUTER VISION AND GRAPHICS (ICCVG 2004)
Keywords
Field
DocType
photometric stereo, non-quadratic optimization, noise reduction
Noise reduction,Computer vision,Infinity,Algorithm,Artificial intelligence,Variables,Bivariate analysis,Gaussian noise,Partial differential equation,Optimization problem,Photometric stereo,Mathematics
Conference
Volume
Citations 
PageRank 
32
6
0.52
References 
Authors
12
2
Name
Order
Citations
PageRank
Ryszard Kozera116326.54
Lyle Noakes214922.67