Shape Analysis of Elastic Curves in Euclidean Spaces

被引:402
|
作者
Srivastava, Anuj [1 ]
Klassen, Eric [2 ]
Joshi, Shantanu H. [3 ]
Jermyn, Ian H. [4 ]
机构
[1] Florida State Univ, Dept Stat, Tallahassee, FL 32306 USA
[2] Florida State Univ, Dept Math, Tallahassee, FL 32306 USA
[3] Univ Calif Los Angeles, Sch Med, Lab Neuro Imaging, Dept Neurol, Los Angeles, CA 90095 USA
[4] Univ Durham, Sci Labs, Dept Math Sci, Durham DH1 3LE, England
基金
美国国家科学基金会;
关键词
Elastic curves; Riemannian shape analysis; elastic metric; Fisher-Rao metric; square-root representations; path straightening method; elastic geodesics; parallel transport; shape models; FACE RECOGNITION;
D O I
10.1109/TPAMI.2010.184
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper introduces a square-root velocity (SRV) representation for analyzing shapes of curves in euclidean spaces under an elastic metric. In this SRV representation, the elastic metric simplifies to the L-2 metric, the reparameterization group acts by isometries, and the space of unit length curves becomes the unit sphere. The shape space of closed curves is the quotient space of (a submanifold of) the unit sphere, modulo rotation, and reparameterization groups, and we find geodesics in that space using a path straightening approach. These geodesics and geodesic distances provide a framework for optimally matching, deforming, and comparing shapes. These ideas are demonstrated using: 1) shape analysis of cylindrical helices for studying protein structure, 2) shape analysis of facial curves for recognizing faces, 3) a wrapped probability distribution for capturing shapes of planar closed curves, and 4) parallel transport of deformations for predicting shapes from novel poses.
引用
收藏
页码:1415 / 1428
页数:14
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