JOURNAL ARTICLE

Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method

Junfeng JingHuanhuan ZhangJing WangPeng‐Fei LiJianyuan Jia

Year: 2012 Journal:   Journal of the Textile Institute Vol: 104 (1)Pages: 18-27   Publisher: Taylor & Francis

Abstract

This paper aims at investigating methods for solving the problem of automated fabric defect detection and classification, which are more essential and important in assuring the fabric quality. The work focuses on two aspects: fabric defect detection and classification. In the experiment, first, the detection texture features for texture defect are extracted using Gabor filters. The method would automatically segment defects from the regular texture. Second, texture features for classification use local binary patterns and Tamura method. Fabric samples are used in evaluation and the experimental results obtained further confirm the designed algorithm achieved a high detection success rate.

Keywords:
Local binary patterns Texture (cosmology) Artificial intelligence Pattern recognition (psychology) Gabor filter Computer science Computer vision Feature extraction Image (mathematics) Histogram

Metrics

62
Cited By
6.05
FWCI (Field Weighted Citation Impact)
29
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Industrial Vision Systems and Defect Detection
Physical Sciences →  Engineering →  Industrial and Manufacturing Engineering
Image Processing Techniques and Applications
Physical Sciences →  Engineering →  Media Technology
Image and Object Detection Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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