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      Constructing reparametrization invariant metrics on spaces of plane curves

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          Abstract

          Metrics on shape space are used to describe deformations that take one shape to another, and to determine a distance between them. We study a family of metrics on the space of curves, that includes several recently proposed metrics, for which the metrics are characterised by mappings into vector spaces where geodesics can be easily computed. This family consists of Sobolev-type Riemannian metrics of order one on the space \(\text{Imm}(S^1,\mathbb R^2)\) of parametrized plane curves and the quotient space \(\text{Imm}(S^1,\mathbb R^2)/\text{Diff}(S^1)\) of unparametrized curves. For the space of open parametrized curves we find an explicit formula for the geodesic distance and show that the sectional curvatures vanish on the space of parametrized and are non-negative on the space of unparametrized open curves. For the metric, which is induced by the "R-transform", we provide a numerical algorithm that computes geodesics between unparameterised, closed curves, making use of a constrained formulation that is implemented numerically using the RATTLE algorithm. We illustrate the algorithm with some numerical tests that demonstrate it's efficiency and robustness.

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          Rattle: A “velocity” version of the shake algorithm for molecular dynamics calculations

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            Groups of Diffeomorphisms and the Motion of an Incompressible Fluid

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              Shape Analysis of Elastic Curves in Euclidean Spaces.

              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 IL(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.
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                Author and article information

                Journal
                25 July 2012
                2014-02-06
                Article
                10.1016/j.difgeo.2014.04.008
                1207.5965
                a685c570-c8df-4286-9361-22cecf9ec1f8

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                58B20, 58D15, 65D18
                Differential Geometry and its Applications 34 (2014), 139-165
                27 pages, 4 figures. Extended version
                math.DG math.NA

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