基于向量机的边缘检测算法优化研究

Research of SVM-Based Edge Detection Algorithm Optimization

该文利用最小二乘向量机(LSSvM)对原始图像每一像素的邻域作灰度曲面的最佳拟合,并以径向基核函数为例导出了图像的梯度算子和零交叉算子。通过梯度和零交叉的综合,实现了边缘的定位和检测,提出了利用边缘检测性能指标来优化参数的方法。确定了高斯LSsVM的参数(σ2,γ)为(7,1),用所选参数进行了图像边缘检测实验。结果表明,基于支持向量机的边缘检测算法可靠性好、效率高。

In this paper, the image intensity surface for the neighborhood of every pixel is well-fitted by the Least Squares Support Vector Machine (LSSVM), and the gradient and the zero-crossing operators are deduced from the LSSVM with the Radial Basis Function (RBF) kernel function, as an example. The decision is made whether a pixel is an edge or not based on the combination results of the gradient and the zero-crossings. One method using the edge detection evaluating merit figure to optimize the LSSVM parameters is proposed. The optimal configuration of parameters (σ2,γ) for the LSSVM with RBF kernel is (7, 1). With the selected parameters, the computer edge detection experiments are carried out. The experimental results demonstrate the proposed algorithm is reliable and efficient.