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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
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.

fabric defectsfeature extractioncomplex contourlet transform(CCT)principal component analysis(PCA)

WU Yi-quan、WAN Hong、YE Zhi-long

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College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Key Laboratory of Textile Science & Technology, Ministry of Education, Donghua University, Shanghai 201620, China

Key Laboratory of Advanced Textile Materials and Manufacturing Technology, Ministry of Education, Zhejiang Sci-Tech University,Hangzhou 310018, China

国家自然科学基金Key Laboratory of Textile Science & Technology,Ministry of Education,ChinaKey Laboratory of Advanced Textile Materials and Manufacturing Technology,Ministry of Education,ChinaPriority Academic Program Development of Jiangsu Higher Education Institution,China

60872065P11112010001

2013

东华大学学报(英文版)
东华大学

东华大学学报(英文版)

EI
影响因子：0.091
ISSN：1672-5220
年,卷(期)：2013.30(4)
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