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arXiv 2209.09862math.DG
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欧氏曲线空间上的弹性扩张:理论与算法

Elastic Metrics on Spaces of Euclidean Curves: Theory and Algorithms

Martin Bauer, Nicolas Charon, Eric Klassen, Sebastian Kurtek, Tom Needham, Thomas Pierron

摘要:

统计形状分析领域中的主要目标是在浸没流形的空间(诸如欧几里得空间中的曲线空间)上定义可计算的和信息性的度量。在弹性形状分析框架中所采取的方法是通过从参数化形状空间上的重新参数化不变黎曼度量开始并通过群同态导出商上的度量来定义这样的度量。在实践中,这个商度量是通过在同构群上找到两个形状的配准来计算的。对于欧氏曲线空间,初始黎曼度量通常是从一个两参数的索伯列夫度量族中选择的,称为弹性度量。弹性度量是特别方便的,因为对于几个参数的选择,它们被认为是局部等距黎曼度量,其中一个是能够解决测地线边界问题明确-这些局部等距的著名例子包括复杂的平方根变换Younes,Michor,芒福德和沙阿和平方根速度(SRV)变换的Srivastava,Klassen,乔希和Jermyn。在本文中,我们表明,SRV变换扩展到弹性度量的所有参数的选择,在任何维度的曲线,从而充分概括了许多作者在过去二十年的工作。我们给出了一个统一的治疗的弹性度量:我们延长结果Trouvé和Younes,Bruveris以及Lahiri,罗宾逊和Klassen的注册问题的解决方案的存在,我们开发算法计算距离和测地线,我们将这些算法的度量学习问题,在那里我们学习最佳的弹性度量参数的统计形状分析任务。

英文摘要:

A main goal in the field of statistical shape analysis is to define computable and informative metrics on spaces of immersed manifolds, such as the space of curves in a Euclidean space. The approach taken in the elastic shape analysis framework is to define such a metric by starting with a reparameterization-invariant Riemannian metric on the space of parameterized shapes and inducing a metric on the quotient by the group of diffeomorphisms. This quotient metric is computed, in practice, by finding a registration of two shapes over the diffeomorphism group. For spaces of Euclidean curves, the initial Riemannian metric is frequently chosen from a two-parameter family of Sobolev metrics, called elastic metrics. Elastic metrics are especially convenient because, for several parameter choices, they are known to be locally isometric to Riemannian metrics for which one is able to solve the geodesic boundary problem explictly -- well-known examples of these local isometries include the complex square root transform of Younes, Michor, Mumford and Shah and square root velocity (SRV) transform of Srivastava, Klassen, Joshi and Jermyn. In this paper, we show that the SRV transform extends to elastic metrics for all choices of parameters, for curves in any dimension, thereby fully generalizing the work of many authors over the past two decades. We give a unified treatment of the elastic metrics: we extend results of Trouvé and Younes, Bruveris as well as Lahiri, Robinson and Klassen on the existence of solutions to the registration problem, we develop algorithms for computing distances and geodesics, and we apply these algorithms to metric learning problems, where we learn optimal elastic metric parameters for statistical shape analysis tasks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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