提出一种基于控制点一致性约束的稳健仿射迭代最近点算法,该方法通过建立控制点约束下的目标函数,引导数据点集仿射配准到目标点集,建立数据点集和目标点集的最近点对应关系,采用随机抽样一致性方法筛选高精度形状控制点,实现控制点约束下的目标函数更新仿射变换。实验结果表明,与传统的图像点集仿射配准算法相比,所提算法的准确性和稳健性显著提高。

A robust affine iterative closest point algorithm is proposed based on a control point consistency constraint. The proposed algorithm establishes an objective function that is constrained by the control points, and the data point set can be affinely registered to a target point set. Furthermore, the nearest point correspondence is established between the data point and target point sets; subsequently, a random sample consensus method is used to select the high-precision shape control points, and the new affine transformation is obtained using an objective function under a control point constraint. The experimental results demonstrate that the accuracy and the robustness of the proposed algorithm are improved significantly when compared with those exhibited by the conventional image point set affine registration algorithms.