By Topic

An automated cotton contamination detection system based on co-occurrence Matrix contrast information

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

6 Author(s)
Mingxiao Ding ; Inst. of Autom., Chinese Acad. of Sci., Beijing, China ; Wei Huang ; Bing Li ; Shaohong Wu
more authors

An automated cotton contamination detection system is economical and efficient to guarantee higher textile quality and lower production cost. A vision system is proposed to realize a fully automated cotton inspection scheme. In the system, cotton contamination is detected based on texture feature. Gray Level Co-occurrence Matrix (GLCM) algorithm is adopted to detect the sharp contrast objects. A rotating search filter based on contextual information is designed to remove the unwanted edges and locate the coordinate of impurities. Experiments using real imagery show that the proposed vision system is suitable to distinguish impurities mixed in cotton.

Published in:

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on  (Volume:4 )

Date of Conference:

20-22 Nov. 2009