Functional data analysis (FDA) is a fast-growing area of research and development in
statistics. While most FDA literature imposes the classical L 2 Hilbert structure on function …

Abstract Symmetric Positive Definite (SPD) matrices are ubiquitous in data analysis under
the form of covariance matrices or correlation matrices. Several O (n)-invariant Riemannian …

Abstract Conventional Computed Tomography (CT) imaging recognition faces two
significant challenges:(1) There is often considerable variability in the resolution and size of …

Human brain functional connectivity (FC) is often measured as the similarity of functional
MRI responses across brain regions when a brain is either resting or performing a task. This …

In this article, we propose a new approach for facial expression recognition (FER) using
deep covariance descriptors. The solution is based on the idea of encoding local and global …

Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for
accurately visualizing the change and development of the brain's subcortical structures (eg …

This chapter reviews some past and recent developments in shape comparison and
analysis of curves based on the computation of intrinsic Riemannian metrics on the space of …

In many applications, the data can be represented by covariance matrices or correlation
matrices between several signals (EEG, MEG, fMRI), physical quantities (cells, genes), or …

Abstract Symmetric Positive Definite (SPD) matrices have been widely used in medical data
analysis and a number of different Riemannian metrics were proposed to compute with …

This paper uses sample data to study the problem of comparing populations on finite-
dimensional parallelizable Riemannian manifolds and more general trivial vector bundles …