Identification of surface defects in textured materials using wavelet packets

Research output: Journal article publicationConference articleAcademic researchpeer-review

25 Citations (Scopus)


This paper investigates a new approach for the detection of surface defects, in textured materials, using wavelet packets. Every inspection image is decomposed with a family of real orthonormal wavelet bases. The wavelet packet coefficients from a set of dominant frequency channels containing significant information are used for the characterization of textured images. A fixed number of shift invariant measures from the wavelet packet coefficients are computed. The magnitude and position of these shift invariant measures in a quadtree representation forms the feature set for a two-layer neural network classifier. The neural net classifier classifies these feature vectors into either of defect or defect-free classes. The experimental results suggest that this proposed scheme can successfully identify the defects, and can be used for automated visual inspection.
Original languageEnglish
Pages (from-to)247-251
Number of pages5
JournalConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
Publication statusPublished - 1 Jan 2001
Externally publishedYes
Event36th IAS Annual Meeting -Conference Record of the 2001 Industry Applications - Chicago, IL, United States
Duration: 30 Sept 20014 Oct 2001

ASJC Scopus subject areas

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


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