光学图像中的抛物线目标特征检测方法研究

针对光学图像中的抛物线目标特征检测问题，提出一种基于最小二乘法的抛物线检测方法。采用Canny边缘检测算子进行边缘提取，对提取到的边缘点采用最小二乘法确定抛物线方程的系数，通过对二次曲线施加抛物线约束，使得算法不需要经过迭代就能得到最优解，并对抛物线方程进行坐标变换推导，计算出抛物线的参数。仿真数据实验和实际图像的实验表明:该算法能够检测到光学图像中的抛物线目标特征，各个参数的计算精度均在98%以上。

Feature detection method for parabola object in optical image

The parabola object feature detection of optical image is a difficult research topic in computer vision fields. A parabola feature detection algorithm based on the least square method is presented. Using Canny algorithm to detect edge, then the least square method is used to compute the coefficients of the parabola equation. The conic is constrained by parabola and the algorithm, so it can get the optimal solution without iteration computation. Finally the coordinate transformation of the parabola equation is computed and the parameters of parabola can be achieved. The experimental results of emulation data and real image show that our algorithm can efficiently detect the parabola object feature and compute the feature parameters in the optical image, and the calculation accuracy of each parameter is above 98%.