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- W2359688575 abstract "For multiscale geometric analysis based feature extraction method of surface defects,the ordinary separable two-dimensional wavelet bases are isotropic,resulting in being incapable of capturing texture and edge,and the usual features extracted from decomposition coefficients are not comprehensive.A feature extraction method based on nonsubsampled contourlet transform(NSCT)and pulse coupled neural networks(PCNN)was proposed and applied to automatic recognition of cold rolled steel strip surface defects.Firstly the defect image was decomposed into multiscale and multidirection with NSCT.Then the subband image was input to PCNN to generate firing maps whose entropy sequence was calculated as the subband's feature.The features of all subbands were combined to produce the feature vector of the original image.Finally the samples were classified by the support vector machine.The method could extract image feature comprehensively and accurately,especially direction information such as edge and texture.Furthermore,it has high computational parallelism and PCNN needs not to be trained.The feature extraction method was examined with samples of surface defects collected from a cold rolled steel strip production line,and the recognition rate was 95.44%." @default.
- W2359688575 created "2016-06-24" @default.
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- W2359688575 date "2013-01-01" @default.
- W2359688575 modified "2023-09-26" @default.
- W2359688575 title "Automatic Recognition Method of Surface Defects Based on Nonsubsampled Contourlet Transform and PCNN" @default.
- W2359688575 hasPublicationYear "2013" @default.
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