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| Research on Re-extraction Algorithm of Paper Defect Characteristics Based on PCA |
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| DOI：10.11981/j.issn.1000-6842.2019.03.54 |
| Key Words:paper defect features; feature dimension; principal component analysis; detection algorithm; computation amount |
| Fund Project:陕西省教育厅专项科技项目(16JK1105)；陕西省科技攻关项目(2016GY-005)；咸阳市科技计划项目(2017K02-06)。 |
| Author Name | Affiliation | | WANG Siqi* | School of Electrical and Control Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 | | ZHOU Qiang | School of Electrical and Control Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 | | TIAN Xingzhi | School of Electrical and Control Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 |
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| Abstract: |
| Because of the low accuracy in identification of similar paper defects in traditional paper defect detection and the slow running speed of the system caused by high feature dimension extraction， a PCA-based paper defect feature re-extraction algorithm was proposed. This method took various paper defect images as the research object， PCA was adoped to deal with the dimension reduction of high-dimensional original features that may have correlations and remove their related components so as to form new defect features which were indepen-dent and more representative， so that the data processing amount was reduced. At the same time， the identification accuracy of paper defects could be significantly improved. Experiments showed that the algorithm could significantly improve the accuracy of paper defect identification and the average running time of the system was greatly shortened． |
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