Edge detection using simplified Gabor wavelets

Wei Jiang, Kin Man Lam, Ting Zhi Shen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Gabor wavelets (GWs) have been commonly used for extracting local features for various applications, such as recognition, tracking, and edge detection. However, extracting the Gabor features is computationally intensive, so the features may be impractical for real-time applications. In this paper, we propose a set of simplified version of Gabor wavelets (SGWs) for edge detection. Experimental results show that our SGW-based edge detection algorithm can achieve a similar performance level to that using GWs, while the runtime required for feature extraction using SGWs is faster than that with GWs with the use of the fast Fourier transform (FFT). When compared to the Canny and other conventional edge detection methods, our proposed method can achieve a better performance in the terms of detection accuracy and computational complexity.
Original languageEnglish
Title of host publication2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP
Pages586-591
Number of pages6
DOIs
Publication statusPublished - 24 Sep 2008
Event2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP - Zhenjiang, China
Duration: 7 Jun 200811 Jun 2008

Conference

Conference2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP
CountryChina
CityZhenjiang
Period7/06/0811/06/08

Keywords

  • Edge detection
  • Gabor wavelets
  • Simplified Gabor wavelets

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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