Imaging Components, Systems, and Processing

Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

[+] Author Affiliations
Junfeng Jing, Xiaoting Fan, Pengfei Li

Xi’an Polytechnic University, School of Electronic and Information, 19 South Road, Jinhua, Xi’an 710048, China

Opt. Eng. 55(5), 053109 (May 26, 2016). doi:10.1117/1.OE.55.5.053109
History: Received December 21, 2015; Accepted April 26, 2016
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Abstract.  Automatic patterned fabric defect detection is a promising technique for textile manufacturing due to its low cost and high efficiency. The applicability of most existing algorithms, however, is limited by their intensive computation. To overcome or alleviate the problem, this paper presents a convolutional matching pursuit (CMP) dual-dictionary algorithm for patterned fabric defect detection. A preprocessing with mean sampling is performed to eliminate the influence of background texture of fabric defects. Subsequently, a set of defect-free image blocks are selected as a sample set by sliding window. Dual-dictionary and sparse coefficiencies of the defect-free sample set are obtained via CMP and the K-singular value decomposition (K-SVD) based on a Gabor filter. Then we employ the defect-free and defective fabric image’s projections onto the dual-dictionary as features for defect detection. Finally, the test results are determined by comparing the distance between the features to be measured. Experimental results reveal that the proposed algorithm is effective for patterned fabric defect detection and an acceptable average detection rate reaches by 94.2%.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Junfeng Jing ; Xiaoting Fan and Pengfei Li
"Patterned fabric defect detection via convolutional matching pursuit dual-dictionary", Opt. Eng. 55(5), 053109 (May 26, 2016). ; http://dx.doi.org/10.1117/1.OE.55.5.053109


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