基于支持向量机理论的植物根系图像边缘检测方法

由于传统边缘检测方法中存在噪声大、粗糙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.为此,基于支持向量机方法给出了一种改善的边缘检测算法.同时,提出了边缘检测算法流程,然后使用支持向量机分类方法对图像进行边缘检测；用所得到的边缘检测算法与Canny算法和Prewitt算法的性能进行了比较.仿真结果表明,给出的算法与Canny算法和Prewitt算相比,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.

Edge Detection Method of Plant Roots Image Based on Support Vector Machine Theory

Considering the disadvantages in the traditional image edge detection methods,such as the noise of the edge,rough 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.An improved image edge detection algorithm method based on support vector machine(SVM) was proposed to solve above problems.Algorithm flow is proposed firstly,and then perform the detection using the SVM classification.The performance of the presented edge detection algorithm is compared with Canny detectors and Prewitt operators.The experimental result demonstrates that,compared with Canny and Prewitt edge detection methods,the proposed edge detection is not only performance improved edge detection,but can be overcome to a certain extent noise interference.