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Optimal Gabor Filtering for the Inspection of Striped Fabric

  • Le Tong
  • Xiaoping Zhou
  • Jiajun Wen
  • Can Gao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

As an important part of products’ quality control, automatic fabric inspection has attracted much attention in the past. Compared with manual inspection, automatic inspection can achieve not only more accurate detection results but also a higher efficiency. With the diversified fabric texture and patterns, it is very necessary to develop distinctive detection methods for different types of fabric. In this paper, based on optimal Gabor filters, a novel defect detection model is proposed to address the inspection of striped fabric, which is commonly used in our daily dresses. In the framework of the detection model, Gabor filters perpendicular to the stripe pattern are optimized to minimize the variance of the image but enhance the features of defects. Thereafter, an adaptive thresholding is set to accurately segment the defective image area. The evaluation of the proposed detection model is conducted using samples of the TILDA database. It is revealed that the common fabric defects as well as the pattern variants could be successfully detected through the proposed detection model.

Keywords

Fabric inspection Optimal Gabor transformation Stripe fabric 

Notes

Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 61703283, 61773328, 61672358, 61703169, 61573248, in part by the Research Grant of The Hong Kong Polytechnic University (Project Code:G-YBD9 and G-YBD9), in part by the China Postdoctoral Science Foundation under Project 2016M590812, Project 2017T100645 and Project 2017M612736, in part by the Guangdong Natural Science Foundation under Project 2017A030310067, Project with the title Rough Sets-Based Knowledge Discovery for Hybrid Labeled Data and Project with the title The Study on Knowledge Discovery and Uncertain Reasoning in Multi-Valued Decisions.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Le Tong
    • 1
  • Xiaoping Zhou
    • 1
  • Jiajun Wen
    • 2
    • 3
    • 4
  • Can Gao
    • 2
    • 3
  1. 1.College of InformationMechanical and Electrical Engineering, Shanghai Normal UniversityShanghaiChina
  2. 2.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  3. 3.Institute of Textile and ClothingThe Hong Kong Polytechnic UniversityKowloonHong Kong
  4. 4.The Hong Kong Polytechnic University Shenzhen Research InstituteShenzhenChina

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