DocumentCode :
1642724
Title :
Estimating surface characteristics using physical reflectance models
Author :
Ragheb, Hossein ; Hancock, Edwin R.
Author_Institution :
Dept. of Comput. Sci., Univ. of York, UK
Volume :
2
fYear :
2003
Abstract :
We make use of the Beckmann-Kirchhoff and Davies scattering models to estimate surface properties for both dielectric and metallic surfaces based on reflectance measurements. In the case of metallic surfaces, we consider two refinements of the Davies theory, which apply under different restrictions concerning the reflectance geometry. The first of these is due to Bennett and Porteus (1961) and applies for normal incidence and reflectance. The second is due to Torrance (1964) and applies when the incidence radiation is off normal. We then suggest three classes of materials for which the appropriate approximations may be used to estimate the surface roughness, the correlation length and the surface slope. Finally, we use the surface slope estimates to fit the Beckmann-Kirchhoff model to reflectance data. In contrast to previous methods which work at long wavelengths and use special purpose instrumentation, our methods can be performed using visible light and a digital camera.
Keywords :
computational geometry; computer vision; light reflection; light scattering; surface roughness; Beckmann-Kirchhoff scattering model; Davies scattering model; Davies theory; computer graphics; computer vision; correlation length; dielectric surface; digital camera; image texture formation; incidence radiation; metallic surface; normal incidence; physical reflectance model; reflectance data; reflectance geometry; reflectance measurement; surface characteristic estimation; surface roughness; surface slope; visible light; Computer science; Computer vision; Geometry; Image analysis; Optical surface waves; Reflectivity; Rough surfaces; Surface fitting; Surface roughness; Surface texture;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-1900-8
Type :
conf
DOI :
10.1109/CVPR.2003.1211468
Filename :
1211468
Link To Document :
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