A robust nonrigid point set registration framework based on global and intrinsic topological constraints
A robust nonrigid point set registration framework based on global and intrinsic topological constraints
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DOI：
10.1007/s00371-020-02037-7
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Guiqiang Yang;Rui Li;Yujun Liu;Ji Wang
中科院分区：
文献类型：
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作者：
Guiqiang Yang;Rui Li;Yujun Liu;Ji Wang
配准非刚性点集的问题，其目的是估计两个给定点集之间的对应关系并学习它们之间的变换，经常出现在计算机视觉任务中。本文提出了一种新的方法进行非刚性点集配准的数据与各种类型的退化，其中的配准问题被配制为高斯混合模型（GMM）为基础的密度估计问题。具体而言，两个互补的约束条件，共同考虑在GMM概率框架的优化。第一种是基于薄板样条的正则化约束，保持全局空间运动的一致性，第二种是基于谱图的正则化约束，保持点集的内在结构。此外，使用期望最大化算法交替优化对应关系和变换，以获得封闭形式的解。我们首先利用局部描述符来构造初始对应关系，然后在基于GMM的框架下估计潜在的变换。在轮廓图像和真实的图像上的实验结果表明了该方法的有效性和鲁棒性。
The problem of registering nonrigid point sets, with the aim of estimating the correspondences and learning the transformation between two given sets of points, often arises in computer vision tasks. This paper proposes a novel method for performing nonrigid point set registration on data with various types of degradation, in which the registration problem is formulated as a Gaussian mixture model (GMM)-based density estimation problem. Specifically, two complementary constraints are jointly considered for optimization in a GMM probabilistic framework. The first is a thin-plate spline-based regularization constraint that maintains global spatial motion consistency, and the second is a spectral graph-based regularization constraint that preserves the intrinsic structure of a point set. Moreover, the correspondences and the transformation are alternately optimized using the expectation maximization algorithm to obtain a closed-form solution. We first utilize local descriptors to construct the initial correspondences and then estimate the underlying transformation under the GMM-based framework. Experimental results on contour images and real images show the effectiveness and robustness of the proposed method.