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Proceedings Paper

X-ray inspection utilizing knowledge-based feature isolation with a neural network classifier
Author(s): Adam R. Nolan; Yong-Lin Hu; William G. Wee
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Paper Abstract

This paper describes a generalized flaw detection scheme for a molded and machined turbine blade. The data used are radiograph images. Based on knowledge of the molding and machining process, selective features may be isolated and classified for each possible flaw candidate. The proposed classification system requires the incorporation of many smaller pattern recognition systems. Several of these pattern recognition subsystems have been developed and implemented. Described is the implementation of one such subsystem whose characteristics are best realize utilizing a back propagation neural network. The results of the network are compared with other classification schemes (K nearest neighbor and Bayes classifier).

Paper Details

Date Published: 1 August 1991
PDF: 8 pages
Proc. SPIE 1472, Image Understanding and the Man-Machine Interface III, (1 August 1991); doi: 10.1117/12.46480
Show Author Affiliations
Adam R. Nolan, Univ. of Cincinnati (United States)
Yong-Lin Hu, Univ. of Cincinnati (United States)
William G. Wee, Univ. of Cincinnati (United States)

Published in SPIE Proceedings Vol. 1472:
Image Understanding and the Man-Machine Interface III
Eamon B. Barrett; James J. Pearson, Editor(s)

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