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Proceedings Paper

Fabric defect detection based on textured characteristics using wavelet transform
Author(s): Ziguang Sun; Zhiqi Liu; Xiaorong Wang; YiYi Xu
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Paper Abstract

In texture defect detection, the defects can be discriminated according to the distribution ranges of wavelet coefficients between the normal and defective parts of texture images. In traditional texture defect detection methods, the normal parts of texture images have to be trained in advance. In this paper, we propose a novel method to automatically determine the training regions based on the characteristics exhibited by normal and defective texture images. In this way, the detection error can be reduced because of the avoiding of environmental changes.

Paper Details

Date Published: 19 August 2010
PDF: 6 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78200F (19 August 2010); doi: 10.1117/12.867457
Show Author Affiliations
Ziguang Sun, Guangxi Univ. of Technology (China)
Zhiqi Liu, Guangxi Univ. of Technology (China)
Xiaorong Wang, Guangxi Univ. of Technology (China)
YiYi Xu, Guangxi Univ. of Technology (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

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