Smoothing random noise from human head scan data

In obtaining human head range data with a laser scanner, noise results from system errors and rough surfaces. The noise can be reduced with a suitable filter. Two aspects need to be considered in smoothing head scan data: one is finding a filter to eliminate noise without creating new artifacts; the other is determining the parameters of this filter to achieve optimal smoothing. The Gaussian filter has been shown to have unique characteristics which preserve the integrity of the data. A cross-validation method based on regularization theory has been derived for estimating the correct filter size for smoothing head range data. The authors discuss the justification and implementation of the statements above. Generalized cross-validation is derived in the two-dimensional case. Experimental results are presented that show the technique is effective and robust.