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一种用于点集对称一致非线性配准的高效期望最大化-迭代最近点(EM-ICP)算法。

An efficient EM-ICP algorithm for symmetric consistent non-linear registration of point sets.

作者信息

Combès Benoit, Prima Sylvain

机构信息

INSERM, U746, F-35042 Rennes, France.

出版信息

Med Image Comput Comput Assist Interv. 2010;13(Pt 2):594-601. doi: 10.1007/978-3-642-15745-5_73.

Abstract

In this paper, we present a new algorithm for non-linear registration of point sets. We estimate both forward and backward deformations fields best superposing the two point sets of interest and we make sure that they are consistent with each other by designing a symmetric cost function where they are coupled. Regularisation terms are included in this cost function to enforce deformation smoothness. Then we present a two-step iterative algorithm to optimise this cost function, where the two fields and the fuzzy matches between the two sets are estimated in turn. Building regularisers using the RKHS theory allows to obtain fast and efficient closed-form solutions for the optimal fields. The resulting algorithm is efficient and can deal with large point sets.

摘要

在本文中,我们提出了一种用于点集非线性配准的新算法。我们估计正向和反向变形场,以最佳地叠加两个感兴趣的点集,并通过设计一个对称成本函数来确保它们相互一致,在该成本函数中它们是耦合的。正则化项包含在这个成本函数中以增强变形的平滑性。然后我们提出一种两步迭代算法来优化这个成本函数,其中依次估计两个场以及两个集合之间的模糊匹配。使用再生核希尔伯特空间(RKHS)理论构建正则化器能够为最优场获得快速且高效的闭式解。所得算法效率高且能处理大型点集。

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本文引用的文献

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