Supervised Identification Algorithm on Detection of Foreign Fibers in Raw Cotton
Foreign fibers accounted for a small proportion in cotton, but there is serious impact on the quality of textile. Foreign fibers are removed by hand, which is low efficiency. Generally, the methods of fixed threshold are used to identify foreign fibers in cotton, but high speed flow of cotton is easy to result in fluctuations on light, the color of captured images will be changed accordingly, then misidentification possibility will be increased. But the suitable amount of sample libraries are used in the identification algorithm of supervised classification, which eliminate this defect to meet the requirements of accuracy and real-time. In this paper, according to the character of image gray of foreign fibers in cotton, and mathematical model is established. Further, important image features are enhanced by image processing, foreign fibers’ characters are drawn. At last, Euclidean distance and k-nearest neighbor classification are adopted in identification algorithm, and finally foreign fibers are identified.
foreign fibers identification Euclidean distance k-nearest neighbor classification algorithm
Ling Ouyang Hongtao Peng Dongyun Wang Yongping Dan Fanghua Liu
School of Electrical and Information, Zhongyuan University of Technology, Zhengzhou 450007
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
2648-2651
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）