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Proceedings Paper

Intelligent segmentation of industrial radiographic images using neural networks
Author(s): Shaun W. Lawson; Graham A. Parker
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Paper Abstract

An application of machine vision, incorporating neural networks, which aims to fully automate real-time radiographic inspection in welding process is described. The current methodology adopted comprises two distinct stages - the segmentation of the weld from the background content of the radiographic image, and the segmentation of suspect defect areas inside the weld region itself. In the first stage, a back propagation neural network has been employed to adaptively and accurately segment the weld region from a given image. The training of the network is achieved with a single image showing a typical weld in the run which is to be inspected, coupled with a very simple schematic weld 'template'. The second processing stage utilizes a further backpropagation network which is trained on a test set of image data previously segmented by a conventional adaptive threshold method. It is shown that the two techniques can be combined to fully segment radiographic weld images.

Paper Details

Date Published: 3 October 1994
PDF: 11 pages
Proc. SPIE 2347, Machine Vision Applications, Architectures, and Systems Integration III, (3 October 1994); doi: 10.1117/12.188736
Show Author Affiliations
Shaun W. Lawson, Univ. of Surrey (United Kingdom)
Graham A. Parker, Univ. of Surrey (United Kingdom)


Published in SPIE Proceedings Vol. 2347:
Machine Vision Applications, Architectures, and Systems Integration III
Bruce G. Batchelor; Susan Snell Solomon; Frederick M. Waltz, Editor(s)

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