基于误差效应的亚像素边缘检测算法

针对传统亚像素边缘检测算法在实时性、精确性和鲁棒性之间难以平衡的问题，提出了一种基于误差效应的亚像素边缘检测方法。该方法首先建立边缘函数模型，并计算每个像素边缘对应函数方程常数项的近似值和对应误差，然后结合最小二乘法使整体函数模型误差最小，从而获得较为精确的边缘参数。基于标准图像和实际图像的实验测试结果表明，该算法具有快速、鲁棒性强和检测精度高等优点，能够满足实时检测的要求，评价指标优于其他传统算法。

Subpixel Edge Detection Algorithm Based on Error Effects

A sub-pixel edge detection method based on a tanh function model is proposed to address the problem of the difficult balance among real-time performance, accuracy, and robustness of traditional sub-pixel edge detection algorithms. First, an edge function model is established, and the approximate value and error of the constant term of the function equation corresponding to each pixel edge are calculated. Combined with the least-squares method, the overall functional model error is then minimized to obtain more accurate edge parameters. Experimental results based on both standard and actual images show that the proposed algorithm has advantages such as fast speed, strong robustness, and high detection accuracy and can meet the requirements of real-time detection using better evaluation indicators than other traditional algorithms.