Efficient edge detection using simplified Gabor wavelets

Authors:
Wei Jiang;Kin-Man Lam;Ting-Zhi Shen
Affiliations:
Dept. of Electronic Eng., School of Inf. Sci. and Technology, Beijing Inst. of Technology, Beijing, China and Centre for Signal Processing, Dept. of Electronic and Information Eng., The Hong Kong ...;Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing, China
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Year:
2009

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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 GWs (SGWs) and an efficient algorithm for extracting the features 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. 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.