Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Lishui Central Hospital, Lishui, 323000, China.
Sci Rep. 2018 Jun 7;8(1):8742. doi: 10.1038/s41598-018-26288-6.
A new accurate and robust non-rigid point set registration method, named DSMM, is proposed for non-rigid point set registration in the presence of significant amounts of missing correspondences and outliers. The key idea of this algorithm is to consider the relationship between the point sets as random variables and model the prior probabilities via Dirichlet distribution. We assign the various prior probabilities of each point to its correspondences in the Student's-t mixture model. We later incorporate the local spatial representation of the point sets by representing the posterior probabilities in a linear smoothing filter and get closed-form mixture proportions, leading to a computationally efficient registration algorithm comparing to other Student's-t mixture model based methods. Finally, by introducing the hidden random variables in the Bayesian framework, we propose a general mixture model family for generalizing the mixture-model-based point set registration, where the existing methods can be considered as members of the proposed family. We evaluate DSMM and other state-of-the-art finite mixture models based point set registration algorithms on both artificial point set and various 2D and 3D point sets, where DSMM demonstrates its statistical accuracy and robustness, outperforming the competing algorithms.
提出了一种新的精确鲁棒的非刚性点集配准方法，称为 DSMM，用于存在大量缺失对应和异常值的非刚性点集配准。该算法的关键思想是将点集之间的关系视为随机变量，并通过狄利克雷分布对先验概率进行建模。我们将每个点的各种先验概率分配给其在学生 t 混合模型中的对应点。然后，我们通过在线性平滑滤波器中表示后验概率，结合点集的局部空间表示，得到封闭形式的混合比例，与其他基于学生 t 混合模型的方法相比，这是一种计算效率更高的配准算法。最后，通过在贝叶斯框架中引入隐藏随机变量，我们提出了一个通用的混合模型族，用于推广基于混合模型的点集配准，其中现有的方法可以被视为所提出的族的成员。我们在人工点集和各种 2D 和 3D 点集上评估了 DSMM 和其他最先进的有限混合模型基于的点集配准算法，DSMM 表现出了其统计准确性和鲁棒性，优于竞争算法。
