基于Franklin矩的亚像素级图像边缘检测算法

为了满足计算机视觉标定与精密测量对图像边缘定位的精确度高和抗噪性强的要求,提出一种基于Franklin矩的亚像素级图像边缘检测算法。首先,建立亚像素边缘模型,利用各级Franklin矩的卷积来提取图像边缘点的细节特征;然后,依据Franklin矩的旋转不变性原理,分析图像边缘旋转至垂直方向后各级Franklin矩之间的关系,从而确定图像中亚像素边缘的关键参数;最后,根据改进的边缘判断条件,确定图像中的实际亚像素边缘点。大量实验结果表明,与基于Zernike矩的亚像素级算法、基于小波变换与Zernike矩结合的亚像素级算法、基于Roberts算子与Zernike矩结合的亚像素级算法相比,本文提出的基于Franklin矩的亚像素级图像边缘检测算法速度更快,精度更高且抗噪性强,更好地满足了对于图像边缘定位稳定可靠及高精度测量的要求。

Sub pixel level image edge detection algorithm based on Franklin moments

In order to meet the requirement of high accuracy and strong anti noise performance of image edge localization in computer vision calibration and precision measurement, a sub pixel level image edge detection algorithm based on Franklin moments is proposed. Firstly, a sub pixel edge model is established to extract the detailed features of image edge points with the convolution of Franklin moments at all levels. Then, according to the rotation invariance principle of Franklin moments, the relationship among different levels of Franklin moments after image edge rotating to the vertical direction is analyzed, and the key parameters of the sub pixel image edge are determined. Finally, the actual sub pixel edge points in the image are located based on the improved edge judgment condition. A large number of experimental results show that compared with Zernike moment sub pixel level image edge detection algorithm, the sub pixel level image edge detection algorithm combining wavelet transform with Zernike moment, and the sub pixel level image edge detection algorithm combining Roberts operator with Zernike moment, the proposed sub pixel level image edge detection algorithm based on Franklin moments possesses higher speed and accuracy and stronger anti noise performance, which better meets the measurement requirements of stability and reliability and high precision in image edge localization.