一种基于支持向量机的植物根系图像边缘检测算法

摘要

由于传统边缘检测方法中存在的比如粗糙边缘、噪声边缘和不准确边缘等缺点,因此在植物根系的研究中,采用传统的图像边缘检测方法检测出来的边缘信息都无法达到令人满意的效果.本文基于支持向量机方法给出一种新型、简单有效的边缘检测算法.基于带高斯径向基核函数的最小二乘支持向量机,得到了一簇梯度算子和相应的二阶导数算子.用所得到的边缘检测算法与Canny和Prewitt算法的性能进行了比较.仿真结果表明本文给出的算法与传统算法相比,不仅边缘检测性能得到提高,而且可以一定程度地克服噪声干扰.%Considering the disadvantages in the traditional image edge detection methods,such as the rough edge,noise of the edge and inaccurate edge location,in the study of plant roots,using the traditional image edge detection method to detect the edge can't obtain satisfactory result.A new efficient image edge detection algorithm method based on support vector machine (SVM) was proposed to solve above problems.Based on least squares SVM with Gaussian radial basis function kernel,a set of the new gradient operators and the corresponding second derivative operators are obtained.The performance of the presented edge detection algorithm is compared with Canny and Prewitt detectors.The experimental result demonstrated that,compared with conventional detection methods,the proposed edge detection could not only improve edge detection properties,but also could overcome the noise interference to a certain degree.