abric Defect Detection Based on Template Correction and Low-Rank Decomposition

被引：0

作者：

Ji X. ^{[1
]}

Liang J. ^{[1
]}

Hou Z. ^{[1
]}

Chang X. ^{[1
]}

Liu W. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Changzhou University, Changzhou

来源：

Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Defect Detection; Fabric with Periodic Pattern; Low-Rank Decomposition; Template Correction;

D O I：

10.16451/j.cnki.issn1003-6059.201903008

中图分类号：

学科分类号：

摘要：

To solve the problem of tensile deformation of periodic fabric, a fabric defect detection method based on template correction and low-rank decomposition is proposed. Firstly, the original image is corrected by the template to reduce the influence of stretching deformation on the detection results. Then, a low-rank correction decomposition model is proposed including a low-rank term, sparse term and correction term. The model can be solved by the alternating direction method to generate a low-rank matrix and a sparse matrix. Finally, the optimal threshold segmentation algorithm is utilized to segment the significant images generated by the sparse matrix. Experiments on standard databases show that the recall rate of the proposed method is improved. © 2019, Science Press. All right reserved.

引用

页码：268 / 277

页数：9

共 21 条

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