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International Conference on Cloud Computing and Security

ICCCS 2018: Cloud Computing and Security pp 173–182Cite as

A Novel Nonlocal Low Rank Technique for Fabric Defect Detection

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11066))

Abstract

In textile industry production, fabric defect inspection is a vital step to ensure the quality of fabric before spreading, cutting and so on. Recently, image characteristic of nonlocal self-similarity (NSS) is widely applied to image denoising due to its effectiveness. Actually, fabric defect detection can be considered as a problem that finds noises in an image. Based on the reason, we propose a simple yet effective method, namely nonlocal low rank approximation (NLRA), for fabric defect detection. In NLRA, an image to be processed is divided into many patches. For a given patch, we search its several similar patches and group them as a matrix. Then, the clean image patch can be reconstructed through solving the low rank approximation of the matrix. Finally, a new image will be synthesized from these estimated patches, the defects can be located by finding the difference between the original fabric image and the reconstructed image. Experimental results prove the validity and feasibility of the proposed NLRA algorithm.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61601235, 61502245, in part by the Natural Science Foundation of Jiangsu Province of China under Grants BK20160972, BK20170768, BK20160964, BK20150849, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grants 16KJB520031, 17KJB520019, in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology (NUIST) under Grant 2243141601019.

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Authors and Affiliations

  1. School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, China

    Jielin Jiang & Yadang Chen

  2. College of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing, China

    Yan Cui

  3. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Guangwei Gao

Authors
  1. Jielin Jiang
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  2. Yan Cui
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  3. Yadang Chen
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  4. Guangwei Gao
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Corresponding author

Correspondence to Yan Cui .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Jiang, J., Cui, Y., Chen, Y., Gao, G. (2018). A Novel Nonlocal Low Rank Technique for Fabric Defect Detection. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11066. Springer, Cham. https://doi.org/10.1007/978-3-030-00015-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-00015-8_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00014-1

  • Online ISBN: 978-3-030-00015-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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