A new efficient SVM-based edge detection method

作者：Zheng S^*; Liu H; Tian JW

来源：Pattern Recognition Letters, 2004, 25(10): 1143-1154.

DOI：10.1016/j.patrec.2004.03.009

• edge detection
• least squares support vector machine
• Gaussian radial basis function kernel
• gradient and zero crossing operators

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.

• 出版日期2004-7-16
• 单位华中科技大学