基于误差效应的亚像素边缘检测算法

Subpixel Edge Detection Algorithm Based on Error Effects

摘要:

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图像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，本文算法具有快速、鲁棒性强和高检测精度等优点，能够满足实时检测的要求，评价指标优于其他传统算法。

Abstract:

Aiming at the problem of difficult balance between real-time performance, accuracy and robustness of traditional sub-pixel edge detection algorithms, a sub-pixel edge detection method based on the tanh function model is proposed. Firstly, this method establishes an edge function model, and calculates the approximate value and corresponding error of the constant term of the function equation corresponding to each pixel edge. Then, combined with the least squares method, the overall function model error is minimized to obtain more accurate edge parameters. Experimental results based on standard images and actual images show that the algorithm in this paper has advantages such as fast speed, strong robustness and high detection accuracy, and can meet the requirements of real-time detection, with evaluation indicators better than other traditional algorithms.