一种基于K-L变换和支持向量机的图像分割算法

Bearing defects detection based on support vector machines

提出了一种基于K-L变换和支持向量机结合的图像分割算法,该算法把轴承中的非缺陷区域和缺陷区域分别看作两种不同的纹理模式,先利用可K-l变换对图像进行降维处理,然后用支持向量机方法对两类不同的样本采样学习,最后进行分类判断。实验结果表明,该算法能够较好地实现图像的分割,有着深入研究的价值。

This paper explores an algorithm about dearing surface defects detection by support vector machines that is the new branch of machine learning , in which the defective area and non-defective area are treated as two different textures and are sampled respectively to be learned, in order to reduce dimension, the image data can be processed by PCA. It is shown that this algorithm works well in defects detection.