Defect detection of patterned fabric by spectral estimation technique and rough set classifier

Li, M and Wang, L 2013, 'Defect detection of patterned fabric by spectral estimation technique and rough set classifier', in 2013 Fourth Global Congress on Intelligent Systems, Hong Kong, 3-4 December 2013, pp. 190-194.


Document type: Conference Paper
Collection: Conference Papers

Title Defect detection of patterned fabric by spectral estimation technique and rough set classifier
Author(s) Li, M
Wang, L
Year 2013
Conference name 2013 Fourth Global Congress on Intelligent Systems
Conference location Hong Kong
Conference dates 3-4 December 2013
Proceedings title 2013 Fourth Global Congress on Intelligent Systems
Publisher IEEE Computer Society
Start page 190
End page 194
Total pages 5
Abstract A novel method for patterned fabric defect detection and classification using spectral estimation technique and rough set theory is presented in this paper. Estimating Signal Parameter via Rotational Invariance Technique (ESPRIT) is firstly used to extract the pattern from the image of the patterned fabric. Then, the shape and location of the flawed areas are detected by comparing the pattern image and the source image. A rough set classifier is trained and tested to detect the types of defects in the patterned fabric image. Experimental results show that this method can successfully analyze and recognize oil warp and weft defects in patterned fabrics with nearly 96% success rate.
Subjects Textile Technology
DOI - identifier 10.1109/GCIS.2013.36
Copyright notice © 2013 IEEE
ISBN 9781479928859
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