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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.