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Proceedings Paper

Using neural networks to automatically inspect the defects of textured objects
Author(s): Xiaoliang Xing; Aidong Zhang
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Paper Abstract

A neural network system to automatically inspect the defects of textured objects is presented. The system is composed of two parts, the first part consists of a self-organizing neural network that's main task is to segment the image into different regions which are of different texture characteristics; the second part consists of a neural network that's architecture is similar to that of a Boltzmann machine. Its main goal is to restore the image with defects to a perfect one. All defects can be detected by simply comparing the two images. Our method is optimum for textured objects, because it needs neither the precise registration which is necessary for many inspection methods and difficult to realize, nor the image pre-treatment which is always time consuming.

Paper Details

Date Published: 18 January 1993
PDF: 11 pages
Proc. SPIE 1713, International Conference on Manufacturing Automation, (18 January 1993); doi: 10.1117/12.138490
Show Author Affiliations
Xiaoliang Xing, Zhejiang Univ. (China)
Aidong Zhang, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 1713:
International Conference on Manufacturing Automation

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