International Journal of Computer Theory and Engineering

Editor-In-Chief: Prof. Mehmet Sahinoglu
Frequency: Quarterly
ISSN: 1793-8201 (Print), 2972-4511 (Online)
Publisher:IACSIT Press

OPEN ACCESS
4.0
CiteScore

IJIET 2013 Vol.5(1): 114-117
doi: 10.7763/IJCTE.2013.V5.658

Fabric Defect Detection Using Auto-Correlation Function

Elham Hoseini , Farnoush Farhadi , Farshad Tajeripour

  • Department of Computer sciences and Engineering, Shiraz University, Iran.

Abstract

This paper introduces a new fabric segmentation approach for detecting fabric defects using auto-correlation function. This proposed approach consists of 4 steps: 1) calculating the texture primitive template by auto-correlation function from defect free fabric image in train phase, 2) enhancing the defect areas, through calculation the difference between each texture primitive template and texture image, 3) constructing the mean image to reduce high frequent information of background image, and 4) compute a perfect automatic threshold to present a binary image as a defect pattern. At the end of paper, validity and robustness of the new approach were proved by some experiments done on different defect types. The results indicate that proposed method is implementable on both patterned and unpatterned fabrics.<br /> At the end of paper, validity and robustness of the new approach were proved by some experiments done on different defect types. The results indicate that proposed method is implementable on both patterned and unpatterned fabrics.

Keywords

  • Texture primitive template
  • defect pattern
  • image enhancement
  • defect segmentation
658-W10031

How to Cite

Copied

Elham Hoseini, Farnoush Farhadi, and Farshad Tajeripour, "Fabric Defect Detection Using Auto-Correlation Function," International Journal of Computer Theory and Engineering, vol. 5, no. 1, pp. 114-117, 2013. https://doi.org/10.7763/IJCTE.2013.V5.658

Copyright & License

Copyright © 2013 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Article Metrics in Dimensions

Menu