东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

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

ISSN号：1673-9086
期刊名称：《传承》
时间：0
分类：TP391.4[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] TS103.7[轻工技术与工程—纺织工程;轻工技术与工程—纺织科学与工程]
作者机构：[1]College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China, [2]Key Laboratory of Textile Science & Technology, Ministry of Education, Donghua University, Shanghai 201620, China, [3] Key Laboratory of Advanced Textile Materials and Manufacturing Technology, Ministry of Education, Zhejiang Sci-Tech University,Hangzhou 310018, China
相关基金：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

中文摘要：

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 proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,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 wavelet 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 effectively.The recognition rate is greatly improved while the dimension is reduced.

英文摘要：

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 proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,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 effectively.The recognition rate is greatly improved while the dimension is reduced.

同期刊论文项目