DocumentCode :
2538164
Title :
Edge localization in surface reconstruction using optimal estimation theory
Author :
Mathur, S. ; Ferrie, F.P.
Author_Institution :
SOFTIMAGE/Microsoft Corp., Montreal, Que., Canada
fYear :
1997
fDate :
17-19 Jun 1997
Firstpage :
833
Lastpage :
838
Abstract :
Many relaxation based smoothing methods used in surface reconstruction algorithms filter out the effect of noise in image data, but result in the elimination of important discontinuity information as well. In this paper the inter-pixel interaction during relaxation is shown to be equivalent to a multiple measurement fusion problem which can be solved using optimal estimation theory. Pixels in a given neighbourhood act as noisy information sources, combining their information to update the state of that neighbourhood. By formulating discontinuities as another “noise” source in the image, and by using the so-called Curvature Consistency reconstruction algorithm on range images, it is shown that optimal estimation theory offers a method for the automatic and adaptive localization of discontinuities while providing a smooth piece wise continuous surface description
Keywords :
edge detection; image reconstruction; smoothing methods; solid modelling; curvature consistency; edge localization; multiple measurement fusion; optimal estimation; optimal estimation theory; piece wise continuous surface description; smoothing methods; surface reconstruction; Estimation theory; Filtering theory; Gratings; Image reconstruction; Information filtering; Information filters; Iterative algorithms; Reconstruction algorithms; Smoothing methods; Surface reconstruction;
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.1997.609424
Filename :
609424
Link To Document :
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