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 main objective of this book is to introduce the reader to a new way of analyzing object
data, that primarily takes into account the geometry of the spaces of objects measured on the …

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 …

It is common to reduce the dimensionality of data before applying classical multivariate
analysis techniques in statistics. Persistent homology, a recent development in …

Motivated by the problem of nonparametric inference in high level digital image analysis, we
introduce a general extrinsic approach for data analysis on Hilbert manifolds with a focus on …

Three-dimensional medical imaging enables detailed understanding of osteoarthritis
structural status. However, there remains a vast need for automatic, thus, reader …

Most phylogenetic analyses result in a sample of trees, but summarizing and visualizing
these samples can be challenging. Consensus trees often provide limited information about …

Continuous shape change is represented as curves in the shape space. A method for
checking the closeness of these curves to a geodesic is presented. Three large databases of …