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Open Access

Fabric Defect Detection Using Modified Local Binary Patterns

EURASIP Journal on Advances in Signal Processing20072008:783898

Received: 24 December 2006

Accepted: 4 October 2007

Published: 12 November 2007


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.


Information TechnologyQuantum InformationDefect DetectionTest ImageGray Scale

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Authors’ Affiliations

Department of Electrical Engineering, Tarbiat Modarres University, Tehran, Iran
Department of Electrical and Electronics Engineering, Shiraz University, Shiraz, Iran


© F. Tajeripour et al 2008

This article is published under license to BioMed Central Ltd. 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.