Texture defect detection using support vector machines with adaptive gabor wavelet features

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

This paper aims at investigating a method for detecting defects on textured surfaces using a Support Vector Machines (SVM) classification approach with Gabor wavelet features. Instead of using all the filters in the Gabor wavelets, an adaptive filter selection scheme is applied to reduce the computational cost on feature extraction while keeping a reasonable detection rate. One-Against-All strategy is adopted to prepare the training data for a binary SVM classifier that is learnt to classify pixels as defective or non-defective. Experimental results on comparison with other multiresolution features and the Learning Vector Quantization (LVQ) classifier demonstrate the effectiveness of the proposed method on defect detection on textured surfaces.

Original languageEnglish
Title of host publicationProceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005
Pages275-280
Number of pages6
DOIs
StatePublished - 2005
Event7th IEEE Workshop on Applications of Computer Vision, WACV 2005 - Breckenridge, CO, United States
Duration: Jan 5 2005Jan 7 2005

Publication series

NameProceedings - Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005

Conference

Conference7th IEEE Workshop on Applications of Computer Vision, WACV 2005
Country/TerritoryUnited States
CityBreckenridge, CO
Period1/5/051/7/05

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software

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