A new efficient SVM-based edge detection method

Authors:
Sheng Zheng;Jian Liu;Jin Wen Tian
Affiliations:
State Education Commission Key Laboratory for Image Processing and Intelligent Control, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology ...;State Education Commission Key Laboratory for Image Processing and Intelligent Control, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology ...;State Education Commission Key Laboratory for Image Processing and Intelligent Control, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology ...
Venue:
Pattern Recognition Letters
Year:
2004

Citing 13
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Abstract

An innovative edge detection algorithm, using both the gradients and the zero crossings to locate the edge positions, is presented in this paper. Based on the least squares support vector machine (LS-SVM) with Gaussian radial basis function kernel, a set of the new gradient operators and the corresponding second derivative operators are obtained. Computer experiments are carried out for extracting edge information from real images and sharp image edges are obtained from a variety of sample images. Some of the best results are attained from a number of standard test problems. The performance of the proposed algorithm is compared with many other existing methods, including Sobel and Canny detectors. The experimental results indicate that the proposed edge detector is near equal to the Canny in the performance and is fast in the speed.