In video based face recognition, great success has been made by representing videos as
linear subspaces, which typically lie in a special type of non-Euclidean space known as …

In this paper, we examine image and video-based recognition applications where the
underlying models have a special structure-the linear subspace structure. We discuss how …

Learning representations on Grassmann manifolds is popular in quite a few visual
recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper …

We present an approach for comparing two sequences of deforming shapes using both
parametric models and nonparametric methods. In our approach, Kendall's definition of …

Crame/spl acute/r-Rao bounds on estimation accuracy are established for estimation
problems on arbitrary manifolds in which no set of intrinsic coordinates exists. The frequently …

Photovoltaic (PV) array analytics and control have become necessary for remote solar farms
and for intelligent fault detection and power optimization. The management of a PV array …

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 …

Many applications in computer vision and pattern recognition involve drawing inferences on
certain manifold-valued parameters. In order to develop accurate inference algorithms on …

Recent advances in computer vision and machine learning suggest that a wide range of
problems can be addressed more appropriately by considering non-Euclidean geometry. In …

In recent years there has been a growing interest in problems, where either the observed
data or hidden state variables are confined to a known Riemannian manifold. In sequential …