高斯拟合亚像素边缘检测算法

Sub-pixel edge detection algorithm based on Gauss fitting

针对传统边缘检测算法的定位精度低、对噪声敏感等缺点,提出基于函数曲线拟合的亚像素边缘检测算法——梯度方向高斯曲线拟合亚像素定位算法。该方法首先在边缘附近选取一系列点,求得这些点的灰度值,进而求得这些点的梯度值,然后运用高斯曲线来对这些点的梯度值进行拟合,最后通过拟合曲线求得高斯曲线的对称轴位置即为亚像素位置。实验表明该算法能够很好地实现亚像素定位,通过与其他两种亚像素定位算法的比较,得出该算法运行时间较短,效率较高。

Concerning the low accuracy in localization and sensitivity to noise in traditional edge detection algorithms, a sub-pixel edge detection algorithm based on function curve fitting, Gauss fitting of gradient direction sub-pixel edge detection algorithm was proposed. This method firstly chosed a series of points near the edge, then got the grey level of these points, and then tried to get the gradient level of these points. Then Gauss curves were used to fit the gradient levels of these points. Finally the axis of the Gauss curves was got by fitting, and the position of axis would be the sub-pixel edge position. The experimental results show that this algorithm can localize the sub-pixel edge position accurately. The comparision with other two algorithms shows that the running time of this algorithm is shorter, and the efficiency is relatively higher.