A new voting approach to texture defect detection based on multiresolutional decomposition

B. B.M. Moasheri, S. Azadinia, A. Mehbodniya

Research output: Contribution to journalArticlepeer-review

Abstract

Wavelets have provided the researchers with significant positive results, by entering the texture defect detection domain. The weak point of wavelets is that they are one-dimensional by nature so they are not efficient enough to describe and analyze two-dimensional functions. In this paper we present a new method to detect the defect of texture images by using curvelet transform. Simulation results of the proposed method on a set of standard texture images confirm its correctness. Comparing the obtained results indicates the ability of curvelet transform in describing discontinuity in two-dimensional functions compared to wavelet transform.

Original languageEnglish
Pages (from-to)887-891
Number of pages5
JournalWorld Academy of Science, Engineering and Technology
Volume65
Publication statusPublished - 2010 May 1

Keywords

  • Curvelet
  • Defect detection
  • Wavelet

ASJC Scopus subject areas

  • Engineering(all)

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