Simplifying Transforms for General Elastic Metrics on the Space of Plane Curves.

IF 2.1 3区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
SIAM Journal on Imaging Sciences Pub Date : 2020-01-01 Epub Date: 2020-03-12 DOI:10.1137/19m1265132
Tom Needham, Sebastian Kurtek
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引用次数: 21

Abstract

In the shape analysis approach to computer vision problems, one treats shapes as points in an infinite-dimensional Riemannian manifold, thereby facilitating algorithms for statistical calculations such as geodesic distance between shapes and averaging of a collection of shapes. The performance of these algorithms depends heavily on the choice of the Riemannian metric. In the setting of plane curve shapes, attention has largely been focused on a two-parameter family of first order Sobolev metrics, referred to as elastic metrics. They are particularly useful due to the existence of simplifying coordinate transformations for particular parameter values, such as the well-known square-root velocity transform. In this paper, we extend the transformations appearing in the existing literature to a family of isometries, which take any elastic metric to the flat L 2 metric. We also extend the transforms to treat piecewise linear curves and demonstrate the existence of optimal matchings over the diffeomorphism group in this setting. We conclude the paper with multiple examples of shape geodesics for open and closed curves. We also show the benefits of our approach in a simple classification experiment.

平面曲线空间上一般弹性度量的简化变换。
在计算机视觉问题的形状分析方法中,人们将形状视为无限维黎曼流形中的点,从而促进了统计计算的算法,例如形状之间的测地线距离和形状集合的平均。这些算法的性能在很大程度上取决于黎曼度量的选择。在平面曲线形状的设定中,注意力主要集中在一阶索博列夫度量的双参数族,称为弹性度量。由于存在针对特定参数值的简化坐标变换,例如众所周知的平方根速度变换,因此它们特别有用。在本文中,我们将现有文献中出现的变换推广到一组等距图,这些等距图取任意弹性度规到平坦的l2度规。我们还扩展了这些变换来处理分段线性曲线,并证明了在这种情况下微分同构群上存在最优匹配。最后给出了开曲线和闭曲线的形状测地线的多个例子。我们还在一个简单的分类实验中展示了我们的方法的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
3.80
自引率
4.80%
发文量
58
审稿时长
>12 weeks
期刊介绍: SIAM Journal on Imaging Sciences (SIIMS) covers all areas of imaging sciences, broadly interpreted. It includes image formation, image processing, image analysis, image interpretation and understanding, imaging-related machine learning, and inverse problems in imaging; leading to applications to diverse areas in science, medicine, engineering, and other fields. The journal’s scope is meant to be broad enough to include areas now organized under the terms image processing, image analysis, computer graphics, computer vision, visual machine learning, and visualization. Formal approaches, at the level of mathematics and/or computations, as well as state-of-the-art practical results, are expected from manuscripts published in SIIMS. SIIMS is mathematically and computationally based, and offers a unique forum to highlight the commonality of methodology, models, and algorithms among diverse application areas of imaging sciences. SIIMS provides a broad authoritative source for fundamental results in imaging sciences, with a unique combination of mathematics and applications. SIIMS covers a broad range of areas, including but not limited to image formation, image processing, image analysis, computer graphics, computer vision, visualization, image understanding, pattern analysis, machine intelligence, remote sensing, geoscience, signal processing, medical and biomedical imaging, and seismic imaging. The fundamental mathematical theories addressing imaging problems covered by SIIMS include, but are not limited to, harmonic analysis, partial differential equations, differential geometry, numerical analysis, information theory, learning, optimization, statistics, and probability. Research papers that innovate both in the fundamentals and in the applications are especially welcome. SIIMS focuses on conceptually new ideas, methods, and fundamentals as applied to all aspects of imaging sciences.
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