Improved Edge Detection Based on LS-SVM

Abstract

In this paper, the image is filtered with the adaptive multi-scale Gauss filter based on intensity image's mean square value histogram, that is, with the statistical information of the image itself. And the intensity surface of the image filtered by the adaptive multi-scale Gauss filter for the neighborhood of every pixel is well-fitted by the least squares support vector machine (LS-SVM), the gradient and the zero-crossing operators are deduced from the LS-SVM with the radial basis function (RBF) kernel. And then the decision is made as to whether a pixel is an edge or not based on the combination results of the gradient and the zero-crossings. Computer experiments are carried out. Compared with the LS-SVM with RBF kernel function by using single scale parameter Gauss filter to suppress the noise, the experimental results demonstrate the proposed algorithm is efficient, especially, when the SNR is lower.