2,675 research outputs found
Elastic shape matching of parameterized surfaces using square root normal fields.
In this paper we define a new methodology for shape analysis of parameterized surfaces, where the main issues are: (1) choice of metric for shape comparisons and (2) invariance to reparameterization. We begin by defining a general elastic metric on the space of parameterized surfaces. The main advantages of this metric are twofold. First, it provides a natural interpretation of elastic shape deformations that are being quantified. Second, this metric is invariant under the action of the reparameterization group. We also introduce a novel representation of surfaces termed square root normal fields or SRNFs. This representation is convenient for shape analysis because, under this representation, a reduced version of the general elastic metric becomes the simple \ensuremathL2\ensuremathL2 metric. Thus, this transformation greatly simplifies the implementation of our framework. We validate our approach using multiple shape analysis examples for quadrilateral and spherical surfaces. We also compare the current results with those of Kurtek et al. [1]. We show that the proposed method results in more natural shape matchings, and furthermore, has some theoretical advantages over previous methods
Gauge Invariant Framework for Shape Analysis of Surfaces
This paper describes a novel framework for computing geodesic paths in shape
spaces of spherical surfaces under an elastic Riemannian metric. The novelty
lies in defining this Riemannian metric directly on the quotient (shape) space,
rather than inheriting it from pre-shape space, and using it to formulate a
path energy that measures only the normal components of velocities along the
path. In other words, this paper defines and solves for geodesics directly on
the shape space and avoids complications resulting from the quotient operation.
This comprehensive framework is invariant to arbitrary parameterizations of
surfaces along paths, a phenomenon termed as gauge invariance. Additionally,
this paper makes a link between different elastic metrics used in the computer
science literature on one hand, and the mathematical literature on the other
hand, and provides a geometrical interpretation of the terms involved. Examples
using real and simulated 3D objects are provided to help illustrate the main
ideas.Comment: 15 pages, 11 Figures, to appear in IEEE Transactions on Pattern
Analysis and Machine Intelligence in a better resolutio
Constructing reparametrization invariant metrics on spaces of plane curves
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 of
parametrized plane curves and the quotient space 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.Comment: 27 pages, 4 figures. Extended versio
Numerical inversion of SRNFs for efficient elastic shape analysis of star-shaped objects.
The elastic shape analysis of surfaces has proven useful in several application areas, including medical image analysis, vision, and graphics.
This approach is based on defining new mathematical representations of parameterized surfaces, including the square root normal field (SRNF), and then using the L2 norm to compare their shapes. Past work is based on using the pullback of the L2 metric to the space of surfaces, performing statistical analysis under this induced Riemannian metric. However, if one can estimate the inverse of the SRNF mapping, even approximately, a very efficient framework results: the surfaces, represented by their SRNFs, can be efficiently analyzed using standard Euclidean tools, and only the final results need be mapped back to the surface space. Here we describe a procedure for inverting SRNF maps of star-shaped surfaces, a special case for which analytic results can be obtained. We test our method via the classification of 34 cases of ADHD (Attention Deficit Hyperactivity Disorder), plus controls, in the Detroit Fetal Alcohol and Drug Exposure Cohort study. We obtain state-of-the-art results
The Square Root Velocity Framework for Curves in a Homogeneous Space
In this paper we study the shape space of curves with values in a homogeneous
space , where is a Lie group and is a compact Lie subgroup. We
generalize the square root velocity framework to obtain a reparametrization
invariant metric on the space of curves in . By identifying curves in
with their horizontal lifts in , geodesics then can be computed. We can also
mod out by reparametrizations and by rigid motions of . In each of these
quotient spaces, we can compute Karcher means, geodesics, and perform principal
component analysis. We present numerical examples including the analysis of a
set of hurricane paths.Comment: To appear in 3rd International Workshop on Diff-CVML Workshop, CVPR
201
A discrete framework to find the optimal matching between manifold-valued curves
The aim of this paper is to find an optimal matching between manifold-valued
curves, and thereby adequately compare their shapes, seen as equivalent classes
with respect to the action of reparameterization. Using a canonical
decomposition of a path in a principal bundle, we introduce a simple algorithm
that finds an optimal matching between two curves by computing the geodesic of
the infinite-dimensional manifold of curves that is at all time horizontal to
the fibers of the shape bundle. We focus on the elastic metric studied in the
so-called square root velocity framework. The quotient structure of the shape
bundle is examined, and in particular horizontality with respect to the fibers.
These results are more generally given for any elastic metric. We then
introduce a comprehensive discrete framework which correctly approximates the
smooth setting when the base manifold has constant sectional curvature. It is
itself a Riemannian structure on the product manifold of "discrete curves"
given by a finite number of points, and we show its convergence to the
continuous model as the size of the discretization goes to infinity.
Illustrations of optimal matching between discrete curves are given in the
hyperbolic plane, the plane and the sphere, for synthetic and real data, and
comparison with dynamic programming is established
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