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- W2022320998 abstract "In the textile manufacturing industry, fabric defect detection becomes a necessary and essential step in quality control. The investment in this field is more than economical when reduction in labor cost and associated benefits are considered. Moreover, the development of a wholly automated inspection system requires efficient and robust algorithms. To overcome this problem, in this paper, we present a new fabric defect detection scheme which uses the local homogeneity and neural network. Its first step consists in computing a new homogeneity image denoted as <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:mi>H</mml:mi></mml:mrow></mml:math>-image. The second step is devoted to the application of the discrete cosine transform (DCT) to the <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mrow><mml:mi>H</mml:mi></mml:mrow></mml:math>-image and the extraction of different representative energy features of each DCT block. These energy features are used by the back-propagation neural network to judge the existence of fabric defect. Simulations on different fabric images and different defect aspects show that the proposed method achieves an average accuracy of 97.35%." @default.
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- W2022320998 date "2015-03-17" @default.
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- W2022320998 title "Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network" @default.
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- W2022320998 doi "https://doi.org/10.1155/2015/376163" @default.
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