用于快速特征点配准的聚类凸集投影算法

Clustering Successive POCS for Fast Point Matching

为降低特征点配准的计算量,提出了一种聚类凸集投影算法。该算法首先通过聚类将模板点集和目标点集的点配准问题转化为相应的类配准问题,然后将序贯凸集投影算法用于求解该问题,从而得到一种聚类的凸集投影算法。它可以看作是序贯凸集投影算法结合聚类思想而得到的推广。由于该算法的误差和计算量取决于类半径的大小,因此在点密度较大的情况下,通过适当选择类半径,可明显降低计算量,而精度只有少许降低。仿真结果表明,该算法是有效的。

A clustering successive projection onto convex sets algorithm is presented for fast point matching. Via feature point clustering, the problem of matching two point sets is converted to that of matching corresponding clusters, which is then solved by a tailored successive projection onto covex sets（SPOCS） algorithm. The resulting algorithm can be viewed as an extention of SPOCS by combining with clustering. Its precision and computational complexity are decided by the clustering radius. Under the condition that the point sets＇ density is high, by choosing a proper radius, the computational burden can be reduced with only negligible deterioration of precision. Experimental results demonstrate the effectiveness of the algorithm.