DocumentCode :
2533753
Title :
Photometric computation of the sign of Gaussian curvature using a curve-orientation invariant
Author :
Angelopoulou, Elli ; Wolff, Lawrence B.
Author_Institution :
Comput. Vision Lab., Johns Hopkins Univ., Baltimore, MD, USA
fYear :
1997
fDate :
17-19 Jun 1997
Firstpage :
432
Lastpage :
437
Abstract :
The authors compute the sign of Gaussian curvature using a purely geometric definition. Consider a point p on a smooth surface S and a closed curve γ on S which encloses p. The image of γ on the unit normal Gaussian sphere is a new curve β. The Gaussian curvature at p is defined as the ratio of the area enclosed by γ over the area enclosed by β as γ contracts to p. The sign of Gaussian curvature at p is determined by the relative orientations of the closed curves γ and β. They directly compare the relative orientation of two such curves from intensity data. They employ three unknown illumination conditions to create a photometric scatter plot. This plot is in one-to-one correspondence with the subset of the unit Gaussian sphere containing the mutually illuminated surface normals. This permits direct computation of the sign of Gaussian curvature without the recovery of surface normals. Their method is albedo invariant. They assume diffuse reflectance, but the nature of the diffuse reflectance can be general and unknown. Simulations, as well as empirical results, demonstrate the accuracy of the technique
Keywords :
computational geometry; computer vision; feature extraction; image classification; image segmentation; object recognition; photometry; reflectivity; simulation; Gaussian curvature sign; albedo invariant method; closed curve; curve-orientation invariant; diffuse reflectance; intensity data; mutually illuminated surface normals; photometric computation; photometric scatter plot; purely geometric definition; relative closed curve orientation; simulation; smooth surface; unit Gaussian sphere; unit normal Gaussian sphere; unknown illumination conditions; Computer science; Computer vision; Contracts; Gaussian processes; Laboratories; Lighting; Photometry; Reflectivity; Scattering; Surface fitting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on
Conference_Location :
San Juan
ISSN :
1063-6919
Print_ISBN :
0-8186-7822-4
Type :
conf
DOI :
10.1109/CVPR.1997.609361
Filename :
609361
Link To Document :
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