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 p...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.展开更多
An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-freque...An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-frequency components,respectively,and finally an image is synthesized.Subjective-visual-quality of the image fusion result is compared with those of the image fusion methods based on NMF and the combination of wavelet /contourlet /nonsubsampled contourlet with NMF.The experimental results are evaluated quantitatively,and the running time is also contrasted.It is shown that the proposed image fusion method can gain larger information entropy,standard deviation and mean gradient,which means that it can better integrate featured information from all source images,avoid background noise and promote space clearness in the fusion image effectively.展开更多
基金National Natural Science Foundation of China(No.60872065)the Key Laboratory of Textile Science&Technology,Ministry of Education,China(No.P1111)+1 种基金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
文摘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.
基金Supported by National Natural Science Foundation of China (No. 60872065)
文摘An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-frequency components,respectively,and finally an image is synthesized.Subjective-visual-quality of the image fusion result is compared with those of the image fusion methods based on NMF and the combination of wavelet /contourlet /nonsubsampled contourlet with NMF.The experimental results are evaluated quantitatively,and the running time is also contrasted.It is shown that the proposed image fusion method can gain larger information entropy,standard deviation and mean gradient,which means that it can better integrate featured information from all source images,avoid background noise and promote space clearness in the fusion image effectively.
基金The National Natural Science Foundation of China(61573183)The Open Fund of Jiangsu Key Laboratory of Big Data Analysis Technology(KXK1403)+3 种基金The Open Fund of Zhejiang Province Key Laboratory for Signal Processing(ZJKL_6_SP-OP 2014-02)The Open Fund of Guangxi Key Lab of Multi-Source Information Mining and Security(MIMS14-01)The Open Fund of Key Laboratory of Geo-Spatial Information Technology(KLGSIT2015-05)The Open Fund of MLR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral Resources(ZS1406)
文摘针对红外图像与SAR图像的灰度差异性大、两者融合图像不太符合人类视觉认知的问题,提出了一种基于联合稀疏表示的复Contourlet域红外图像与SAR图像融合方法。首先对红外图像与SAR图像分别进行复Contourlet分解。然后利用K-奇异值分解(K-Singular Value Decomposition,K-SVD)方法获得两幅源图像低频分量的过完备字典,并根据联合稀疏表示模型生成联合字典,通过正交匹配追踪(Orthogonal Matching Pursuit,OMP)方法求出源图像低频分量在联合字典下的稀疏表示系数,接着采用选择最大化策略对两个低频分量的稀疏表示系数进行选取,随后进行稀疏表示重构获得融合的低频分量;对高频分量结合视觉敏感度系数和能量匹配度两个活跃度准则进行融合,以捕获源图像丰富的细节信息。最后经复Contourlet逆变换获得融合图像。与3种经典融合方法及近年来提出的基于非下采样Contourlet变换(Non-Subsampled Contourlet Transform,NSCT)、基于稀疏表示的融合方法相比,该方法能够有效突出源图像的显著特征,最大程度地继承源图像的信息。
