By Topic

Detection of Weft Knitting Fabric Defects Based on Windowed Texture Information And Threshold Segmentation by CNN

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sun Yao ; Coll. of Textiles, DongHua Univ., Shanghai, China ; Long Hai-ru

Methods for detecting weft knitting fabric defects are studied in this article. A new method to analyze the texture information on the fabric image with multi-window for enhancing the defects feature is introduced. The feature information of defect is segmented by cellular neural network and three terms of variables are defined to represent the feature. Using interlock fabric with the defects of hole, course mark, dropped stitch and fly as experiment materials, the experiment proved the acquired feature information involved adequate information of defects with less effect of noise and the result of classification by artificial neural network was well performed.

Published in:

Digital Image Processing, 2009 International Conference on

Date of Conference:

7-9 March 2009