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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Robust point matching for nonrigid shapes by preserving local neighborhood structures
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Robust point matching for nonrigid shapes by preserving local neighborhood structures

机译：通过保留局部邻域结构进行非刚性形状的鲁棒点匹配

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摘要

In previous work on point matching, a set of points is often treated as an instance of a joint distribution to exploit global relationships in the point set. For nonrigid shapes, however, the local relationship among neighboring points is stronger and more stable than the global one. In this paper, we introduce the notion of a neighborhood structure for the general point matching problem. We formulate point matching as an optimization problem to preserve local neighborhood structures during matching. Our approach has a simple graph matching interpretation, where each point is a node in the graph, and two nodes are connected by an edge if they are neighbors. The optimal match between two graphs is the one that maximizes the number of matched edges. Existing techniques are leveraged to search for an optimal solution with the shape context distance used to initialize the graph matching, followed by relaxation labeling updates for refinement. Extensive experiments show the robustness of our approach under deformation, noise in point locations, outliers, occlusion, and rotation. It outperforms the shape context and TPS-RPM algorithms on most scenarios.
机译：在以前的点匹配工作中，通常将一组点视为联合分布的一个实例，以利用点集中的全局关系。但是，对于非刚性形状，相邻点之间的局部关系比整体形状更牢固，更稳定。在本文中，我们介绍了一般点匹配问题的邻域结构概念。我们将点匹配公式化为优化问题，以在匹配过程中保留局部邻域结构。我们的方法具有简单的图匹配解释，其中每个点都是图中的一个节点，如果两个节点是邻居，则两个节点通过一条边连接。两个图之间的最佳匹配是最大化匹配边缘数的图。现有技术被利用具有用于初始化图形匹配的形状上下文距离来搜索最佳解决方案，然后放松标签更新以进行细化。大量的实验表明我们的方法在变形，点位置的噪声，离群值，遮挡和旋转下的鲁棒性。在大多数情况下，它的性能都优于形状上下文和TPS-RPM算法。

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