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Paper Defects Classification Based on Multi-scale Image Enhancement Combined with Convolution Neural Network
  
DOI:10.11980/j.issn.0254-508X.2018.08.009
Key Words:image enhancement  convolution neural network(CNN)  multi-scale morphological gradient  image classification
Fund Project:国家自然科学基金(61461025);国家自然科学基金(61811530325);中国博士后科学基金项目(2016M602856)。
Author NameAffiliation
LI Guang-ming College of Electrical and Information Engineering Shaanxi University of Science and Technology Xi’an Shaanxi Province 710021 
XUE Ding-hua College of Electrical and Information Engineering Shaanxi University of Science and Technology Xi’an Shaanxi Province 710021 
JIA Xiao-hong College of Electrical and Information Engineering Shaanxi University of Science and Technology Xi’an Shaanxi Province 710021 
LI Yun-tong College of Electrical and Information Engineering Shaanxi University of Science and Technology Xi’an Shaanxi Province 710021 
LEI Tao* College of Electrical and Information Engineering Shaanxi University of Science and Technology Xi’an Shaanxi Province 710021 
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Abstract:In paper industry, traditional approaches of paper defects classification depend on the selection of feature descriptors and the selector.To address the problem, an approach of paper defects classification based on multi-scale image enhancement combined with convolution neural network(CNN) was proposed in this paper-Firstly, multi-scale morphological gradient was computed and used to enhance the edge contour information of paper image, the gradient features of defects in a paper image was highlighted.Then, CNN was applied to learn image features and employed to classify paper defects images.Finally, paper defects classification with a high accuracy was achieved.The experimental results showed that the proposed approach obtained a higher accuracy, 98.44%, 99.23%, and 99.64% on Caltech101, KTH-TIPS, and our datasets, respectively, was significantly superior to the methods of HOG+SVM, LBP+SVM, and traditional CNN.Compared to current methods for paper defects classification, the proposed approach did not require to extract defects features using feature descriptors.
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