In this study, we provide an overview of recent advances in multisensor multitarget tracking
based on the random finite set (RFS) approach. The fusion that plays a fundamental role in …

The metric d (A, B)= tr A+ tr B− 2 tr (A 1∕ 2 BA 1∕ 2) 1∕ 2 1∕ 2 on the manifold of n× n
positive definite matrices arises in various optimisation problems, in quantum information …

This paper presents a new technique for computing the barycenter of a set of distance or
kernel matrices. These matrices, which define the inter-relationships between points …

The firstgeneration of brain-computer interfaces (BCI) classifies multi-channel
electroencephalographic (EEG) signals, enhanced by optimized spatial filters. The second …

This paper addresses the problem of distributed multitarget detection and tracking based on
the linear arithmetic average (AA) fusion. We first analyze the conservativeness and Fréchet …

We present group equivariant capsule networks, a framework to introduce guaranteed
equivariance and invariance properties to the capsule network idea. Our work can be …

Covariance matrices have attracted attention for machine learning applications due to their
capacity to capture interesting structure in the data. The main challenge is that one needs to …

We develop geometric optimization on the manifold of Hermitian positive definite (HPD)
matrices. In particular, we consider optimizing two types of cost functions:(i) geodesically …

Covariance matrices have found success in several computer vision applications, including
activity recognition, visual surveillance, and diffusion tensor imaging. This is because they …

Hermitian positive definite (hpd) matrices form a self-dual convex cone whose interior is a
Riemannian manifold of nonpositive curvature. The manifold view comes with a natural …