Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns

Authors:
F. Tajeripour;E. Kabir
Affiliations:
-;-
Venue:
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Year:
2007

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Abstract

Local Binary Patterns, LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) 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 simple and gray scale invariant. Because of its simplicity, online implementation is possible as well. Key words: Defect Detection, Texture, Local Binary Patterns, Fabric, Machine Vision.