34,464 research outputs found
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
Shape analysis on homogeneous spaces: a generalised SRVT framework
Shape analysis is ubiquitous in problems of pattern and object recognition
and has developed considerably in the last decade. The use of shapes is natural
in applications where one wants to compare curves independently of their
parametrisation. One computationally efficient approach to shape analysis is
based on the Square Root Velocity Transform (SRVT). In this paper we propose a
generalised SRVT framework for shapes on homogeneous manifolds. The method
opens up for a variety of possibilities based on different choices of Lie group
action and giving rise to different Riemannian metrics.Comment: 28 pages; 4 figures, 30 subfigures; notes for proceedings of the Abel
Symposium 2016: "Computation and Combinatorics in Dynamics, Stochastics and
Control". v3: amended the text to improve readability and clarify some
points; updated and added some references; added pseudocode for the dynamic
programming algorithm used. The main results remain unchange
Shape analysis on Lie groups and homogeneous spaces
In this paper we are concerned with the approach to shape analysis based on
the so called Square Root Velocity Transform (SRVT). We propose a
generalisation of the SRVT from Euclidean spaces to shape spaces of curves on
Lie groups and on homogeneous manifolds. The main idea behind our approach is
to exploit the geometry of the natural Lie group actions on these spaces.Comment: 8 pages, Contribution to the conference "Geometric Science of
Information '17
Understanding General Relativity after 100 years: A matter of perspective
This is the centenary year of general relativity, it is therefore natural to
reflect on what perspective we have evolved in 100 years. I wish to share here
a novel perspective, and the insights and directions that ensue from it.Comment: v1, 16pp; contribution to Paddy's Festschrif
Extreme-value statistics from Lagrangian convex hull analysis for homogeneous turbulent Boussinesq convection and MHD convection
We investigate the utility of the convex hull of many Lagrangian tracers to
analyze transport properties of turbulent flows with different anisotropy. In
direct numerical simulations of statistically homogeneous and stationary
Navier-Stokes turbulence, neutral fluid Boussinesq convection, and MHD
Boussinesq convection a comparison with Lagrangian pair dispersion shows that
convex hull statistics capture the asymptotic dispersive behavior of a large
group of passive tracer particles. Moreover, convex hull analysis provides
additional information on the sub-ensemble of tracers that on average disperse
most efficiently in the form of extreme value statistics and flow anisotropy
via the geometric properties of the convex hulls. We use the convex hull
surface geometry to examine the anisotropy that occurs in turbulent convection.
Applying extreme value theory, we show that the maximal square extensions of
convex hull vertices are well described by a classic extreme value
distribution, the Gumbel distribution. During turbulent convection,
intermittent convective plumes grow and accelerate the dispersion of Lagrangian
tracers. Convex hull analysis yields information that supplements standard
Lagrangian analysis of coherent turbulent structures and their influence on the
global statistics of the flow.Comment: 18 pages, 10 figures, preprin
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