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中文摘要：
支持向量机是一种新的机器学习方法。它建立在统计学习理论基础上，较好地解决了小样本的学习问题。由于其出色的学习性能，该技术已经成为当前国际机器学习界的研究热点。文中提出了一种基于支持向量机的图像边缘检测新方法。这种方法介绍了如何使用支持向量机来高效的检测图像的边缘。首先用几个边缘简单的图像对支持向量机进行训练，然后使用支持向量分类方法进行边缘检测。针对实际图像的边缘检测实验表明，支持向量机可以有效地进行图像的边缘检测，其检测效果和传统的Canny边缘检测算子相当。
英文摘要：
Support vector machine （SVM） is a new method of machine learning. It based on the statistical learning theory, and can setde ＂small＂ example problem well. In this paper, a new method for edge detection based on support vector machine is presented. This method shows how the SVM can detect edge in an efficient way. First the training is performed using a few own - created images that represent a dearly defined edges with the edges easily located, and then perform the detection using the SVM classification. The results of real images edge detection experiments show that this method is comparable to the classical edge detection methods, such as Canny edge detector.
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