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Chin. Opt. Lett.
 Home  List of Issues    Issue 03 , Vol. 01 , 2003    An artificial immune approach for optical image based vision inspection


An artificial immune approach for optical image based vision inspection
Hong Zheng1, Nanfeng Xiao2, Jinhui Lan3
1School of Electronic Information, Wuhan University, Wuhan 4300792School of Computer Science & Engineering, South China University of Technology, Guangzhou 5106413Department of Precision Instruments, Tsinghua University, Beijing 100084

Chin. Opt. Lett., 2003, 01(03): pp.142-142-

DOI:
Topic:Instrumentation, measurement, and metrology
Keywords(OCIS Code): 100.5010  150.3040  

Abstract
This paper presents a novel approach of visual inspection for texture surface defects. The approach uses artificial immune theory in learning the detection of texture defects. In this paper, texture defects are regards as non-self, and normal textures are regarded as self. Defect filters and segmentation thresholds used for defect detection are regarded as antibodies. The clonal selection algorithm stemmed from the natural immune system is employed to learn antibodies. Experimental results on textile image inspection are presented to illustrate the merit and feasibility of the proposed method.

Copyright: © 2003-2012 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Received:2002/9/6
Accepted:
Posted online:

Get Citation: Hong Zheng, Nanfeng Xiao, Jinhui Lan, "An artificial immune approach for optical image based vision inspection," Chin. Opt. Lett. 01(03), 142-142-(2003)

Note: This project was partially supported by the National Natural Science Foundation under grant No. 40271094. H. Zheng's e-mail address is zhenghong@21cn.com or hzheng@deakin.edu.au.



References

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2. K. Y. Song, M. Petrou, and J. Kittler, Proc. SPIE 1708, 99 (1992).

3. D. M. Tsai and B. Hsiao, Pattern Recognition 34, 1285 (2001).

4. L. N. De Castro and F. J. Von Zuben, Technical Report, RT DCA 01/99 (Brazil, 1999).


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