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Proceedings Paper

Surface inspection of flat products by means of texture analysis: on-line implementation using neural networks
Author(s): Carlos Fernandez; Carlos Platero; Pascual Campoy; Rafael Aracil
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Paper Abstract

This paper describes some texture-based techniques that can be applied to quality assessment of flat products continuously produced (metal strips, wooden surfaces, cork, textile products, ...). Since the most difficult task is that of inspecting for product appearance, human-like inspection ability is required. A common feature to all these products is the presence of non- deterministic texture on their surfaces. Two main subjects are discussed: statistical techniques for both surface finishing determination and surface defect analysis as well as real-time implementation for on-line inspection in high-speed applications. For surface finishing determination a Gray Level Difference technique is presented to perform over low resolution images, that is, no-zoomed images. Defect analysis is performed by means of statistical texture analysis over defective portions of the surface. On-line implementation is accomplished by means of neural networks. When a defect arises, textural analysis is applied which result in a data-vector, acting as input of a neural net, previously trained in a supervised way. This approach tries to reach on-line performance in automated visual inspection applications when texture is presented in flat product surfaces.

Paper Details

Date Published: 23 November 1994
PDF: 9 pages
Proc. SPIE 2249, Automated 3D and 2D Vision, (23 November 1994); doi: 10.1117/12.196077
Show Author Affiliations
Carlos Fernandez, Univ. Politecnica de Madrid (Spain)
Carlos Platero, Univ. Politecnica de Madrid (Spain)
Pascual Campoy, Univ. Politecnica de Madrid (Spain)
Rafael Aracil, Univ. Politecnica de Madrid (Spain)

Published in SPIE Proceedings Vol. 2249:
Automated 3D and 2D Vision
Rolf-Juergen Ahlers; Donald W. Braggins; Gary W. Kamerman, Editor(s)

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