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arXiv 1603.03480math.DGcs.NAmath.NA
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一种用于曲线空间Sobolev度量的数值框架

A Numerical Framework for Sobolev Metrics on the Space of Curves

Florida State University(佛罗里达州立大学);TU Wien(维也纳工业大学);Brunel University London(布鲁内尔伦敦大学);Freiburg University(弗赖堡大学);Technical University of Denmark(丹麦技术大学)

Martin Bauer, Martins Bruveris, Philipp Harms, Jakob Møller-Andersen

AI总结:

本文提出数值算法解决Sobolev度量下的测地线初值和边界值问题,用于计算Riemann梯度优化下的Karcher均值及主成分分析与聚类。

摘要:

通过在形状集上赋予Riemann度量,可以实现统计形状分析。在参数化或非参数化曲线的形状空间上,二阶及更高阶的Sobolev度量具有低阶度量所没有的优良性质,但其离散化仍缺乏。本文提出算法数值求解这些度量下的测地线初值和边界值问题。这些算法的结合使人们能够在Riemann梯度优化方案中计算Karcher均值,并执行主成分分析和聚类。我们的框架足够通用,可应用于广泛类别的度量。通过分析一组代表HeLa细胞核的形状集合,展示了本方法的有效性。

英文摘要:

Statistical shape analysis can be done in a Riemannian framework by endowing the set of shapes with a Riemannian metric. Sobolev metrics of order two and higher on shape spaces of parametrized or unparametrized curves have several desirable properties not present in lower order metrics, but their discretization is still largely missing. In this paper, we present algorithms to numerically solve the geodesic initial and boundary value problems for these metrics. The combination of these algorithms enables one to compute Karcher means in a Riemannian gradient-based optimization scheme and perform principal component analysis and clustering. Our framework is sufficiently general to be applicable to a wide class of metrics. We demonstrate the effectiveness of our approach by analyzing a collection of shapes representing HeLa cell nuclei.

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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