View in EDS HTML Full Text PDF Full Text

Textile fabric defect detection based on low-rank representation.

Saved in:
Bibliographic Details
Title: Textile fabric defect detection based on low-rank representation.
Authors: Shen, Xubang1, Li, Peng1,2, Zhao, Minghua2, Sui, Liansheng2, Liang, Junli3
Source: Multimedia Tools & Applications. Jan2019, Vol. 78 Issue 1, p99-124. 26p.
Subjects: Eigenvalues, Sparse matrix software, Singular value decomposition, Composite materials, Integrated circuits
Abstract: In this paper, we propose a novel and robust fabric defect detection method based on the low-rank representation (LRR) technique. Due to the repeated texture structure we model a defects-free fabric image as a low-rank structure. In addition, because defects, if exist, change only the texture of fabric locally, we model them with a sparse structure. Based on the above idea, we represent a fabric image into the sum of a low-rank matrix which expresses fabric texture and a sparse matrix which expresses defects. Then, the LRR method is applied to obtain the corresponding decomposition. Especially, in order to make better use of low-rank structure characteristics we propose LRREB (low-rank representation based on eigenvalue decomposition and blocked matrix) method to improve LRR. LRREB is implemented by dividing a image into some corresponding blocked matrices to reduce dimensions and applying eigen-value decomposition (EVD) on blocked matrix instead of singular value decomposition (SVD) on original fabric image, which improves the accuracy and efficiency. No training samples are required in our methods. Experimental results show that the proposed fabric defect detection method is feasible, effective, and simple to be employed. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 134393596
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Textile fabric defect detection based on low-rank representation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shen%2C+Xubang%22">Shen, Xubang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Peng%22">Li, Peng</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Minghua%22">Zhao, Minghua</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Sui%2C+Liansheng%22">Sui, Liansheng</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Liang%2C+Junli%22">Liang, Junli</searchLink><relatesTo>3</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Jan2019, Vol. 78 Issue 1, p99-124. 26p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Eigenvalues%22">Eigenvalues</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+matrix+software%22">Sparse matrix software</searchLink><br /><searchLink fieldCode="DE" term="%22Singular+value+decomposition%22">Singular value decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Composite+materials%22">Composite materials</searchLink><br /><searchLink fieldCode="DE" term="%22Integrated+circuits%22">Integrated circuits</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, we propose a novel and robust fabric defect detection method based on the low-rank representation (LRR) technique. Due to the repeated texture structure we model a defects-free fabric image as a low-rank structure. In addition, because defects, if exist, change only the texture of fabric locally, we model them with a sparse structure. Based on the above idea, we represent a fabric image into the sum of a low-rank matrix which expresses fabric texture and a sparse matrix which expresses defects. Then, the LRR method is applied to obtain the corresponding decomposition. Especially, in order to make better use of low-rank structure characteristics we propose LRREB (low-rank representation based on eigenvalue decomposition and blocked matrix) method to improve LRR. LRREB is implemented by dividing a image into some corresponding blocked matrices to reduce dimensions and applying eigen-value decomposition (EVD) on blocked matrix instead of singular value decomposition (SVD) on original fabric image, which improves the accuracy and efficiency. No training samples are required in our methods. Experimental results show that the proposed fabric defect detection method is feasible, effective, and simple to be employed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=134393596
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11042-017-5263-z
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 99
    Subjects:
      – SubjectFull: Eigenvalues
        Type: general
      – SubjectFull: Sparse matrix software
        Type: general
      – SubjectFull: Singular value decomposition
        Type: general
      – SubjectFull: Composite materials
        Type: general
      – SubjectFull: Integrated circuits
        Type: general
    Titles:
      – TitleFull: Textile fabric defect detection based on low-rank representation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Shen, Xubang
      – PersonEntity:
          Name:
            NameFull: Li, Peng
      – PersonEntity:
          Name:
            NameFull: Zhao, Minghua
      – PersonEntity:
          Name:
            NameFull: Sui, Liansheng
      – PersonEntity:
          Name:
            NameFull: Liang, Junli
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: Jan2019
              Type: published
              Y: 2019
          Identifiers:
            – Type: issn-print
              Value: 13807501
          Numbering:
            – Type: volume
              Value: 78
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Multimedia Tools & Applications
              Type: main
ResultId 1