针对采用高斯滤波器对图像进行滤波会导致图像边缘平滑,人为设定高、低阈值会导致阈值的自适应性差,采用双阈值法去除虚假边缘会导致去除效果不佳等问题,提出改进的Canny边缘检测算法并应用于影像测量领域。首先使用开关中值滤波代替高斯滤波,在去除噪声的同时保留非噪声像素点的灰度值不变,从而提高边缘定位精度;然后采用K-means聚类算法以得到高、低梯度值聚类中心,采用OTSU算法以得到梯度阈值,将两个方法结合,可以实现高、低阈值的自适应;最后采用面积形态学的方法去除图像的干扰边缘。实验结果表明,改进的算法具有定位精度高、自适应性强以及干扰点去除效果好等优点。

In this study, the Canny edge detection algorithm is proposed and applied to the image measurement field to solve the problems of image edge smoothing due to Gaussian filtering, poor self-adaptability of the threshold caused by artificially setting high and low thresholds, and poor removal effect caused by using the double threshold method to remove the false edge. First, the switching median filter instead of the Gaussian filter is used. The gray value of non-noise pixels is kept unchanged while denoising to improve the edge positioning accuracy. Next, the K-means clustering algorithm is employed to obtain the clustering center of the high and low gradient values. The OTSU algorithm is employed to acquire the gradient threshold value. The self-adaptation of the high and low threshold values could be achieved by combining the two methods. Finally, the interference edge of the image is removed by area morphology. The experimental results show that the improved algorithm has the advantages of high positioning accuracy, strong self-adaptability, and good removal effect of disturbance points.