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Proceedings Paper

Three-dimensional object surface identification
Author(s): Mehmet Celenk
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Paper Abstract

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).

Paper Details

Date Published: 27 March 1995
PDF: 12 pages
Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); doi: 10.1117/12.205499
Show Author Affiliations
Mehmet Celenk, Ohio Univ. (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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