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Proceedings Paper

Surface sampling techniques for 3D object inspection
Author(s): Chihhsiong Stone Shih; Lester A. Gerhardt
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Paper Abstract

While the uniform sampling method is quite popular for pointwise measurement of manufactured parts, this paper proposes three novel sampling strategies which emphasize 3D non-uniform inspection capability. They are: (a) the adaptive sampling, (b) the local adjustment sampling, and (c) the finite element centroid sampling techniques. The adaptive sampling strategy is based on a recursive surface subdivision process. Two different approaches are described for this adaptive sampling strategy. One uses triangle patches while the other uses rectangle patches. Several real world objects were tested using these two algorithms. Preliminary results show that sample points are distributed more closely around edges, corners, and vertices as desired for many classes of objects. Adaptive sampling using triangle patches is shown to generally perform better than both uniform and adaptive sampling using rectangle patches. The local adjustment sampling strategy uses a set of predefined starting points and then finds the local optimum position of each nodal point. This method approximates the object by moving the points toward object edges and corners. In a hybrid approach, uniform points sets and non-uniform points sets, first preprocessed by the adaptive sampling algorithm on a real world object were then tested using the local adjustment sampling method. The results show that the initial point sets when preprocessed by adaptive sampling using triangle patches, are moved the least amount of distance by the subsequently applied local adjustment method, again showing the superiority of this method. The finite element sampling technique samples the centroids of the surface triangle meshes produced from the finite element method. The performance of this algorithm was compared to that of the adaptive sampling using triangular patches. The adaptive sampling with triangular patches was once again shown to be better on different classes of objects.

Paper Details

Date Published: 27 March 1995
PDF: 18 pages
Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); doi: 10.1117/12.205498
Show Author Affiliations
Chihhsiong Stone Shih, Rensselaer Polytechnic Institute (United States)
Lester A. Gerhardt, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 2423:
Machine Vision Applications in Industrial Inspection III
Frederick Y. Wu; Stephen S. Wilson, Editor(s)

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