基于Otsu算法与直方图分析的自适应Canny算法的改进

Adaptive Canny algorithm improvement based on Otsu algorithm and histogram analysis

Canny算子在边缘检测领域被广泛应用,但传统的Canny算子需要人为设定梯度阈值和合适的高斯标准差,因此自动化程度不高。为解决Canny算子的自适应性,发展了包括Otsu算法在内的自适应算法。但传统的Otsu算法存在定位精度不高,易受高斯滤波标准差影响等问题。在Otsu算法的基础上,通过对高斯滤波后梯度图像的分析与研究,对最大类间方差进行改进,从而提高自适应算法的稳定性。将改进后的类间方差与类内方差的比值作为阈值选取的评价标准,从而有效提高了自适应算法的定位精度。仿真表明,所提出的改进算法能有效提高Canny算法的定位精度和鲁棒性。

Canny operator is widely used in the field of edge detection,but it has low automation degree because the man-made setting of gradient threshold and suitable Gauss standard deviation.In order to improve the adaptivity of Canny operator,the adaptive algorithm including Otsu algorithm was developed.Unfortunately,the traditional Otsu algorithm has low positional accuracy,and is easy to be influenced by Gauss standard deviation.On the basis of studying the Otsu algorithm,the gradient image after Gaussian filtering is analyzed and researched,and the maximum interclass variance is improved to enhance the stability of adaptive algorithm.The ratio of the improved interclass variance and intraclass variance is taken as the evaluation criterion of threshold selection to improve the positional accuracy of adaptive algorithm.The simulation results show that the improved algorithm proposed in this paper can improve the positioning accuracy and robustness of Canny algorithm effectively.