Fabric Defect Detection Based on Wavelet Characteristics

被引：0
|
作者：
Guan, Shengqi [1 ]
Shi, Xiuhua [1 ]
Cui, Haiying [1 ]
Song, Yuqin [1 ]
机构：
[1] NW Polytech Univ, Coll Marine Engn, Xian 710072, Shanxi, Peoples R China
来源：
PACIIA: 2008 PACIFIC-ASIA WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION, VOLS 1-3, PROCEEDINGS | 2008年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
On the basis of wavelet and singular signal characteristic analysis, a new defect detection method based on wavelet characteristics is presented. Firstly, to select wavelet filters of compact support and high vanishing moment properties are regarded as finite biorthogonal filters. Secondly, the new wavelet with concussive and filters coefficients of centralized distribution is constructed by lifting scheme, which is matching with test fabric texture properties. Lastly, the detail signal feature after wavelet decomposition of fabric image is extracted, and it is compared with the detail signal feature of normal fabric image decomposition to determine whether there exists defect. The experimental result confirms that the proposed method is validity and the detection accuracy is over 92.5%.
引用
收藏
页码：349 / 353
页数：5
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