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。本文基于带高斯径向基核函数的最小二乘支持向量机方法,得到了一簇梯度算子和零交叉算子,用来定位图像边缘,从而得到一种有效的图像边缘检测算法。用所得到的边缘检测算法与Sobel算法和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。
Traditional image edge detection methods of plant roots exhibit some disadvantages, such as rough edge, marginal noise and inaccurate edge location. The edge detection of plant roots by the traditional image edge detection method can＇ t obtain satisfactory results. Based on the least squares support vector machine （SVM） with radially Gaussian kernel function, a set of new gradient operators and zero crossing operators are obtained. An efficient image edge detection algorithm, using the gradients to locate the edge position, is proposed to solve the above problems. The performance of the presented edge detection algorithm is compared with those of Canny and Prewitt detectors. Compared with the conventional detection methods, the detection performance of the proposed edge detection method is improved, and the noise interference, to a certain extent, can also be overcome.
出处
《东北林业大学学报》
CAS
CSCD
北大核心
2012年第8期153-156,共4页
Journal of Northeast Forestry University
基金
中央高校基本科研业务费专项资金(Z02068)
关键词
植物根系
边缘检测
最小二乘支持向量机
高斯径向基核函数
零交叉算子
Plant roots
Edge detection
Least squares support vector machines
Radially Gaussian kernel function
Zero crossing operators
作者简介
吴鹏，男，1980年7月生，东北林业大学机电工程学院，讲师。
通信作者：宋文龙，东北林业大学机电工程学院，教授。E-mail：wlsong139@126．com。
