A thoroughly revised and updated edition of this introduction to modern statistical methods
for shape analysis Shape analysis is an important tool in the many disciplines where objects …

A general framework is laid out for principal component analysis (PCA) on quotient spaces
that result from an isometric Lie group action on a complete Riemannian manifold. If the …

The analysis of ratios of body measurements is deeply ingrained in the taxonomic literature.
Whether for plants or animals, certain ratios are commonly indicated in identification keys …

The fingerprint has, with considerable justification, come to be regarded as the acme of
forensic identification. Over the last century, millions of cases have been resolved world …

For planar landmark based shapes, taking into account the non-Euclidean geometry of the
shape space, a statistical test for a common mean first geodesic principal component (GPC) …

We propose an intrinsic multifactorial model for data on Riemannian manifolds that typically
occur in the statistical analysis of shape. Due to the lack of a linear structure, linear models …

This PhD proposes new Riemannian geometry tools for the analysis of longitudinal
observations of neuro-degenerative subjects. First, we propose a numerical scheme to …

A simple and efficient photometric methodology is presented, covering all steps from field
data acquisition to binarization and allowing for leaf contour modelling. This method …

This article presents an infinite Bayesian mixture of monotonic regression models for
analyzing multiple growth pathways of star-shaped objects growing over time by using …

In many statistical settings, it is assumed that high-dimensional data actually lies on a low-
dimensional manifold. In this perspective, there is a need to generalize statistical methods to …