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Signal, Image and Video Processing

, Volume 12, Issue 7, pp 1395–1401 | Cite as

A new method for detecting texture defects based on modified local binary pattern

  • Mohammad Makaremi
  • Navid RazmjooyEmail author
  • Mehdi Ramezani
Original Paper

Abstract

The modified local binary pattern is a method that can produce high-precision features for detection and diagnosis of texture images; in this paper, a method is proposed to detect the texture defects based on this algorithm. The proposed method includes two main phases. The first phase is based on clustering technique to fabric normal texture modeling, and the second phase is a threshold to decide about the fabric defects selection. The total dataset in this research contains 596 texture images from different databases including Isfahan textile dataset, UHK dataset, products and TILDA dataset. The fabric defects are generated because of pressure cracks and has effects, woof defects, warp defects and spool slacking. Finally, a noticeable detection rate about 91.86% with a higher rate of 92.02% sensitivity is achieved for the total given dataset. All of the reported results from tests are achieved by applying the proposed method on the explained dataset.

Keywords

Fabric defect detection Texture recognition Fabric dataset Modal local binary pattern 

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Young Researchers and Elite ClubIslamic Azad University, Majlesi BranchIsfahanIran
  2. 2.Department of Electrical EngineeringUniversity of TafreshTafreshIran
  3. 3.Department of MathematicsUniversity of TafreshTafreshIran

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