Smoothing Range Data for Curvature Estimation
The calculation of curvature plays a crucial role in the recognition of sculptured objects from range data. The smoothing operation which must precede the calculation of curvature should be considered an important part of this process as it has the potential to alter its results. We take as our goal the selection of an effective scale and method for smoothing range data which minimizes changes in the shape of the object. Toward this end we have examined the effect of two aspects of the smoothing process on detection of extremal points of curvature. First, we discuss the use of cross validation as method of scale selection, and second we look at the results of nonlinear smoothing, in particular the use of anisotropic diffusion. Several recommendations are also made regarding the potential usefulness of smoothing methods which take into account the orientation of the surface.
KeywordsCross Validation Range Data Smoothing Parameter Anisotropic Diffusion Range Image
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