This paper describes a computationally efficient matching method for inspecting 3D objects using their serial cross sections. Object regions of interest in cross-sectional binary images of successive slices are aligned with those of the models. Cross-sectional differences between the object and the models are measured in the direction of the gradient of the cross section boundary. This is repeated in all the cross-sectional images. The model with minimum average cross-sectional difference is selected as the best match to the given object (i.e., no defect). The method is tested using various computer generated surfaces and matching results are presented. It is also demonstrated using Symult S-2010 16-node system that the method is suitable for parallel implementation in massage passing processors with the maximum attainable speedup (close to 16 for S-2010).
Mehmet Celenk, Mehmet Celenk,
"Three-dimensional object surface identification", Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); doi: 10.1117/12.205499; https://doi.org/10.1117/12.205499