Quantitative measurements of surface variations are essential to computer inspection of surface quality. We propose a new surface variation measurement algorithm which computes a surface roughness map via the surface normals. The sampled surface data are first approximated by bivariate polynomial functions under least squares criterion. Surface normals are then estimated from the polynomial coefficients. A roughness metric is determined for each surface point in terms of the cumulative angular differences between pairwise normal vectors in the neighborhood. Experimental results show that the established roughness metric can distinguish subtle details of minor surface variations. The computation required in this algorithm are convolution-based operations. Therefore, the method can be readily implemented in a parallel computing environment for real-time applications.
David Shi Chen, David Shi Chen,
"A New Surface Variation Measurement Algorithm Via Surface Normals", Proc. SPIE 0956, Piece Recognition and Image Processing, (12 October 1988); doi: 10.1117/12.947687; https://doi.org/10.1117/12.947687