一种基于支持向量机的图像边缘检测方法

支持向量机是一种新的机器学习的方法。它以统计学习理论为基础,能够较好地解决小样本的学习问题。由于其出色的学习性能,该技术已成为当前国际机器学习界的研究热点。支持向量函数回归(SVR)是SVM的一个重要分支,它已经成功地应用于系统识别、非线性系统的预测等方面,并取得了较好的效果。文中通过图像的SVR表示,对SVR图像的边缘检测进行了研究。文中算例说明了该方法在实际应用中的可行性。实验结果表明,该算法能有效提高图像边缘检测效果。同时对其他边缘检测方法有一定的借鉴作用。

A SVM- Based Image Edge Detection Method

Support vector machine (SVM) is a new method of machine learning. It bases on the statistical learning theory, and can settle"small" example problem well. Because of its excellent learning capacity, support vector regression (SVR) is an important branch of SVM, SVR has been applied to system identification, nonlinear system prediction and good results have been demonstrated. In this paper, with image representation by support vector regression, make a research on image edge detection.At the end actual examples illuminate the feasibility.The result shows that the algorithm can improve image edge detection method effection. This method can also be applied in other similar image edge detection method.