球面和旋转组上的平行切分最佳传输

IF 1.3 4区 数学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Michael Quellmalz, Léo Buecher, Gabriele Steidl
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引用次数: 0

摘要

切片最优传输基本上是先进行拉顿变换,然后再进行一维最优传输,由于其计算效率高,在各种应用中广受欢迎。在本文中,我们将讨论球面(\mathbb {S}^{d-1})和旋转群(\textrm{SO}(3)\)上的切片最优传输。我们提出了球面的平行切分过程,它同样只需要线的最优变换。我们分析了相应的平行切分最优传输的性质,它特别提供了球面概率度量的旋转不变度量。对于 \textrm{SO}(3)\), 我们引入了一个新的二维拉顿变换并发展了它的奇异值分解。在此基础上,我们提出了在\(\textrm{SO}(3)\)上的切分最优传输。由于瓦瑟斯坦距离被广泛应用于原点计算,我们推导出了与我们的新切片瓦瑟斯坦距离相关的原点计算算法,并提供了 2 球面上的合成数值示例,展示了它们在离散球面度量的自由支撑和固定支撑设置下的行为。就计算速度而言,它们优于现有的半圆切片方法和正则化瓦瑟斯坦原点法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Parallelly Sliced Optimal Transport on Spheres and on the Rotation Group

Parallelly Sliced Optimal Transport on Spheres and on the Rotation Group

Sliced optimal transport, which is basically a Radon transform followed by one-dimensional optimal transport, became popular in various applications due to its efficient computation. In this paper, we deal with sliced optimal transport on the sphere \(\mathbb {S}^{d-1}\) and on the rotation group \(\textrm{SO}(3)\). We propose a parallel slicing procedure of the sphere which requires again only optimal transforms on the line. We analyze the properties of the corresponding parallelly sliced optimal transport, which provides in particular a rotationally invariant metric on the spherical probability measures. For \(\textrm{SO}(3)\), we introduce a new two-dimensional Radon transform and develop its singular value decomposition. Based on this, we propose a sliced optimal transport on \(\textrm{SO}(3)\). As Wasserstein distances were extensively used in barycenter computations, we derive algorithms to compute the barycenters with respect to our new sliced Wasserstein distances and provide synthetic numerical examples on the 2-sphere that demonstrate their behavior for both the free- and fixed-support setting of discrete spherical measures. In terms of computational speed, they outperform the existing methods for semicircular slicing as well as the regularized Wasserstein barycenters.

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来源期刊
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision 工程技术-计算机:人工智能
CiteScore
4.30
自引率
5.00%
发文量
70
审稿时长
3.3 months
期刊介绍: The Journal of Mathematical Imaging and Vision is a technical journal publishing important new developments in mathematical imaging. The journal publishes research articles, invited papers, and expository articles. Current developments in new image processing hardware, the advent of multisensor data fusion, and rapid advances in vision research have led to an explosive growth in the interdisciplinary field of imaging science. This growth has resulted in the development of highly sophisticated mathematical models and theories. The journal emphasizes the role of mathematics as a rigorous basis for imaging science. This provides a sound alternative to present journals in this area. Contributions are judged on the basis of mathematical content. Articles may be physically speculative but need to be mathematically sound. Emphasis is placed on innovative or established mathematical techniques applied to vision and imaging problems in a novel way, as well as new developments and problems in mathematics arising from these applications. The scope of the journal includes: computational models of vision; imaging algebra and mathematical morphology mathematical methods in reconstruction, compactification, and coding filter theory probabilistic, statistical, geometric, topological, and fractal techniques and models in imaging science inverse optics wave theory. Specific application areas of interest include, but are not limited to: all aspects of image formation and representation medical, biological, industrial, geophysical, astronomical and military imaging image analysis and image understanding parallel and distributed computing computer vision architecture design.
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