Using neural networks to automatically inspect the defects of textured objects

Xiaoliang Xing; Aidong Zhang

doi:10.1117/12.138490

18 January 1993 Using neural networks to automatically inspect the defects of textured objects

Xiaoliang Xing, Aidong Zhang

Proceedings Volume 1713, International Conference on Manufacturing Automation; (1993) https://doi.org/10.1117/12.138490
Event: International Conference on Manufacturing Automation, 1992, Hong Kong, Hong Kong

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.

Citation Download Citation

Xiaoliang Xing and Aidong Zhang "Using neural networks to automatically inspect the defects of textured objects", Proc. SPIE 1713, International Conference on Manufacturing Automation, (18 January 1993); https://doi.org/10.1117/12.138490

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available