Defect detection in textured materials using Gabor filters

Ajay Kumar Pathak, Grantham K H Pang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

378 Citations (Scopus)

Abstract

This paper investigates various approaches for automated inspection of textured materials using Gabor wavelet features. A new supervised defect detection approach to detect a class of defects in textile webs is proposed. Unsupervised web inspection using multichannel filtering scheme is investigated. A new data fusion scheme to multiplex the information from the different channels is proposed. Various factors interacting the tradeoff for performance and computational load are discussed. This scheme establishes high computational savings over the previously proposed approaches and results in high quality of defect detection. Final acceptance of visual inspection systems depends on economical aspects as well. Therefore, a new low-cost solution for fast web inspection is also included in this paper. The experimental results conducted on real fabric defects for various approaches proposed in this paper confirm their usefulness.
Original languageEnglish
Pages (from-to)425-440
Number of pages16
JournalIEEE Transactions on Industry Applications
Volume38
Issue number2
DOIs
Publication statusPublished - 1 Mar 2002
Externally publishedYes

Keywords

  • Computer vision
  • Defect detection
  • Gabor filters
  • Gabor wavelets, industrial automation
  • Multichannel filtering
  • Quality assurance
  • Textile industry

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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