Optimal smoothing of three-dimensional head scan data by cross validation

Haian Fang; Joseph H. Nurre

doi:10.1117/12.189140

6 October 1994 Optimal smoothing of three-dimensional head scan data by cross validation

Haian Fang, Joseph H. Nurre

Proceedings Volume 2350, Videometrics III; (1994) https://doi.org/10.1117/12.189140
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

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.

Citation Download Citation

Haian Fang and Joseph H. Nurre "Optimal smoothing of three-dimensional head scan data by cross validation", Proc. SPIE 2350, Videometrics III, (6 October 1994); https://doi.org/10.1117/12.189140

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