利用自适应模糊椭球聚类实现点云分区

点云数据分区是逆向工程中重要而又难以解决的问题。采用自适应模糊椭球聚类算法实现逆向工程中的点云分区,利用凸组合在经典模糊聚类算法中加入平面聚类,凸组合系数利用启发技术根据平面大小自适应选择。避免传统分区算法中微分几何特征量的估算;利用竞争凝聚技术自动确定分区数目;分区结果便于后续几何参数精确提取。实验结果验证了该算法的有效性。

Segmentation of point cloud based on Adaptive Fuzzy c-Elliptotype clustering

Segmentation of point cloud is an important problem in reverse engineering.The Adaptive Fuzzy c-Elliptotype(AFC) clustering algorithm is used to solve segmentation problem for reverse engineering.Fuzzy c-planes clustering is added to the classic fuzzy c-means algorithm by convex combination.The combination coefficient is selected adaptively according to the size of planes by a heuristic technique.The differential geometric features that are necessary in the conventional segmentation algorithm do not need to be computed.The number of clusters is determined by competitive agglomeration technique.The algorithm is benefit to extract geometric parameters of surface accurately in next step.The experiment results prove that this algorithm is valid.