earticle

논문검색

Automated Fabric Defect Detection Based on Multiple Gabor Filters and KPCA

초록

영어

A new detection approach is proposed to detect various uniform and structured fabric defects based on the multiple Gabor filters and Kernel Principal Component Analysis. First of all, images are filtered by multiple Gabor filters with six scales and four orientations to extract feature vectors. After that, the sub-blocks divided from the feature vectors have been fused and the high-dimension data can be reduced by using Kernel Principal Component Analysis. Finally, the similarity matrix is calculated by Euclidean norm and segmented with OTSU threshold method. The experiment has been done by integrating hardware and NI LabVIEW graphical programming language. Experimental results show that proposed algorithm improves feature extraction capability significantly and has high recognition rate.

목차

Abstract
 1. Introduction
 2. Description of Method
  2.1. Image Inspection
  2.2. Calibration Procedure
 3. Experiments Results and Analysis
 4. Automation for Inspection
  4.1. Acquisition System Hardware Selection and Design
  4.2. LabVIEW Graphical User Interface Design
 5. Conclusion
 Acknowledgements
 References

저자정보

  • Junfeng Jing School of Electronic and Information, Xi’an Polytechnic University, Xi’an, 710048, China
  • Xiaoting Fan School of Electronic and Information, Xi’an Polytechnic University, Xi’an, 710048, China
  • Pengfei Li School of Electronic and Information, Xi’an Polytechnic University, Xi’an, 710048, China

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.