A Unified Framework for Nonrigid Point Set Registration via Coregularized Least Squares
A Unified Framework for Nonrigid Point Set Registration via Coregularized Least Squares
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DOI：
10.1109/access.2020.3009255
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发表时间：
2020
期刊：
影响因子：
3.9
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
本文描述了一种对具有不同类型退化（变形、遮挡、噪声和异常值）的数据执行非刚性点集配准的方法。我们通过在基于高斯混合模型（GMM）的学习框架中采用两个拓扑互补的约束，将配准问题表述为混合模型估计问题。第一个约束是基于吉洪诺夫的正则化，它通过集体和连贯地移动点集来保持整体空间连通性。第二个约束是基于图拉普拉斯算子的正则化嵌入，它在配准过程中保留了边缘空间的固有拓扑特征。因此，所提出的方法被命名为核心正则化最小二乘法（Co-RLS）。我们的方法迭代地估计两个给定点集之间的对应关系和非刚性变换。首先，使用特征描述符（即形状上下文）根据轮廓点的排序信息建立对应关系。然后，通过最小化Co-RLS函数来恢复变换，并使用期望最大化方法来更新变换参数和异常值比率。各种类型的合成数据和真实数据的实验结果表明，与其他最先进的方法相比，该方法具有优越的有效性和鲁棒性。
This paper describes a method for performing nonrigid point set registration on data with different kinds of degradation (deformation, occlusion, noise, and outliers). We formulate the registration problem as a mixture model estimation problem by employing two topologically complementary constraints in a Gaussian mixture model (GMM)-based learning framework. The first constraint is Tikhonov-based regularization, which maintains the overall spatial connectivity by moving the point set collectively and coherently. The second constraint is graph-Laplacian-based regularization embedding, which preserves the intrinsic topological characteristics of the marginal space during registration. Hence, the proposed method is named coregularized least squares (Co-RLS). Our method iteratively estimates the correspondences and nonrigid transformation between two given sets of points. First, the correspondences are established subject to the ordering information of the contour points using feature descriptors, i.e., the shape context. Then, the transformation is recovered by minimizing the Co-RLS function, and the expectation maximization method is used to update the transformation parameters and outlier ratios. Experimental results on various types of synthetic and real data show the superior effectiveness and robustness of the proposed method compared with other state-of-the-art methods.