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A Hierarchical Framework for Spectral Correspondence

机译:频谱对应的分层框架

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The modal correspondence method of Shapiro and Brady aims to match point-sets by comparing the eigenvectors of a pairwise point proximity matrix. Although elegant by means of its matrix representation, the method is notoriously susceptible to differences in the relational structure of the point-sets under consideration. In this paper we demonstrate how the method can be rendered robust to structural differences by adopting a hierarchical approach. We place the modal matching problem in a probabilistic setting in which the correspondences between pairwise clusters can be used to constrain the individual point correspondences. To meet this goal we commence by describing an iterative method which can be applied to the point proximity matrix to identify the locations of pairwise modal clusters. Once we have assigned points to clusters, we compute within-cluster and between-cluster proximity matrices. The modal co-efficients for these two sets of proximity matrices are used to compute cluster correspondence and cluster-conditional point correspondence probabilities. A sensitivity study on synthetic point-sets reveals that the method is considerably more robust than the conventional method to clutter or point-set contamination.
机译:Shapiro和Brady的模态对应方法旨在通过比较成对点接近矩阵的特征向量来匹配点集。虽然通过其矩阵表示优雅,但该方法是令人惊叹的易受所考虑的点集合结构的差异的影响。在本文中,我们展示了如何通过采用分层方法来对结构差异稳健的方式。我们将模态匹配问题放在概率设置中,其中成对集群之间的对应关系可用于限制各个点对应关系。为了满足这一目标,我们通过描述一种可以应用于点接近矩阵来识别成对模态集群的位置的迭代方法。一旦我们为群集分配了指向群集,我们会计算群集内部和群集之间的群集接近矩阵。这两组接近矩阵的模态共同效能用于计算簇对应和聚类条件点对应概率。对合成点集的敏感性研究表明,该方法比传统方法杂波或点设定污染的方法相当鲁棒。

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