一种自适应阈值的Canny边缘检测算法

摘要

针对传统Canny算法阁值选择困难的问题,本文提出一种基于最大类间方差法的自适应同步搜索高低阈值的方法.该方法首先根据梯度直方图信息将对应的像素分为三类;其次基于改进的Otsu算法定义评价函数,该评价函数描述了类间方差;最后依次搜索评价函数的一个最大值,自动获取Canny算子的高低阈值.该方法不需要人为设定任何参数.与传统Canny算法、直接Otsu算法的结果比较表明,本文方法对不同的图像都能较好地提取真实边缘,特别是在低对比度图像的边缘提取上,此方法更具有优势.%To overcome the difficulty of threshold selecting in Canny algorithm, an improved method based on Otsu algorithm is proposed to choose the threshold adaptively and simultaneously. Firstly, guided by the gradient histogram of the test image, all the pixels are divided into three classes. Secondly, based on the improved Otsu algorithm, an evaluation function is defined to describe the mean square error among the three classes. Finally, both the high and low thresholds are selected adaptively and independently by searching the maximum values of the evaluation function. Artificial parameter setting is not necessary in this method. Compared with the results from traditional Canny method and Direct Otsu method, the method shows great advantage in extracting the real edges from different images, especially low contrast ones.