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Proceedings Paper

Optimal smoothing of three-dimensional head scan data by cross validation
Author(s): Haian Fang; Joseph H. Nurre
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Paper Abstract

Regularization theory, first developed to solve edge detection problems in computer vision, has been studied in this research in an attempt to obtain an optimal scale for Gaussian filter in smoothing head range data. In regularization theory, both accuracy and smoothness of the resultant data is considered. Based on regularization theory, Generalized Cross Validation is derived for 2D head range data smoothing. Preliminary results have shown it to be an efficient way to obtain an optimal scale of Gaussian filters according to the specific head range data.

Paper Details

Date Published: 6 October 1994
PDF: 8 pages
Proc. SPIE 2350, Videometrics III, (6 October 1994); doi: 10.1117/12.189140
Show Author Affiliations
Haian Fang, Ohio Univ. (United States)
Joseph H. Nurre, Ohio Univ. (United States)


Published in SPIE Proceedings Vol. 2350:
Videometrics III
Sabry F. El-Hakim, Editor(s)

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