Skip to main content
  • Research Article
  • Open access
  • Published:

Fabric Defect Detection Using Modified Local Binary Patterns


Local binary patterns (LBPs) are one of the features which have been used for texture classification. In this paper, a method based on using these features is proposed for fabric defect detection. In the training stage, at first step, LBP operator is applied to an image of defect free fabric, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each of these windows. Based on comparison with the reference feature vector, a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is multiresolution and gray scale invariant and can be used for defect detection in patterned and unpatterned fabrics. Because of its simplicity, online implementation is possible as well.

Publisher note

To access the full article, please see PDF.

Author information

Authors and Affiliations


Corresponding author

Correspondence to F. Tajeripour.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Tajeripour, F., Kabir, E. & Sheikhi, A. Fabric Defect Detection Using Modified Local Binary Patterns. EURASIP J. Adv. Signal Process. 2008, 783898 (2007).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: