Feature Extraction of Fabric Defects Based on Complex Contourlet Transform and Principal Component Analysis

作     者：吴一全 万红 叶志龙 

作者机构：College of Electronic and Information EngineeringNanjing University of Aeronautics and Astronautics Key Laboratory of Textile Science & TechnologyMinistry of EducationDonghua University Key Laboratory of Advanced Textile Materials and Manufacturing TechnologyMinistry of EducationZhejiang Sci-Tech University 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2013年第30卷第4期

页      面：282-286页

核心收录：

学科分类：13[艺术学] 08[工学] 1305[艺术学-设计学（可授艺术学、工学学位）] 0821[工学-纺织科学与工程] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 0807[工学-动力工程及工程热物理] 0804[工学-仪器科学与技术] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 082101[工学-纺织工程] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China(No.60872065) the Key Laboratory of Textile Science&Technology,Ministry of Education,China(No.P1111) the Key Laboratory of Advanced Textile Materials and Manufacturing Technology,Ministry of Education,China(No.2010001) the Priority Academic Program Development of Jiangsu Higher Education Institution,China 

主　　题：fabric defects feature extraction complex contourlet transform(CCT) principal component analysis(PCA)CLC number:TP391.4 TS103.7Document code:AArticle ID:1672-5220(2013)04-0282-05 

摘      要：To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is ***,training samples of fabric defect images are decomposed by ***,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature ***,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types *** recognition rate is greatly improved while the dimension is reduced.