Shape analysis methods have in the past few years become very popular, both for
theoretical exploration as well as from an application point of view. Originally developed for …

In this paper, we study a class of Riemannian metrics on the space of unparametrized
curves and develop a method to compute geodesics with given boundary conditions. It …

Statistical shape analysis can be done in a Riemannian framework by endowing the set of
shapes with a Riemannian metric. Sobolev metrics of order two and higher on shape spaces …

Proliferation of high‐resolution imaging data in recent years has led to substantial
improvements in the two popular approaches for analyzing shapes of data objects based on …

A population quantity of interest in statistical shape analysis is the location of landmarks,
which are points that aid in reconstructing and representing shapes of objects. We provide …

The infinite-dimensional Hilbert sphere S∞ has been widely employed to model density
functions and shapes, extending the finite-dimensional counterpart. We consider the Fréchet …

In this paper, we study simple splines on a Riemannian manifold $ Q $ from the point of view
of the Pontryagin maximum principle (PMP) in optimal control theory. The control problem …

Of concern is the study of the space of curves in homogeneous spaces. Motivated by
applications in shape analysis we identify two curves if they only differ by their …

Signatures in Shape Analysis: An Efficient Approach to Motion Identification | SpringerLink
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Alignment of curve data is an integral part of their statistical analysis, and can be achieved
using model-or optimization-based approaches. The parameter space is usually the set of …