Recently, skeleton-based human action recognition has been receiving significant attention
from various research communities due to the availability of depth sensors and real-time …

Representing images and videos with Symmetric Positive Definite (SPD) matrices, and
considering the Riemannian geometry of the resulting space, has been shown to yield high …

Manifolds are widely used to model non-linearity arising in a range of computer vision
applications. This paper treats statistics on manifolds and the loss of accuracy occurring …

Principal geodesic analysis (PGA) is a generalization of principal component analysis (PCA)
for dimensionality reduction of data on a Riemannian manifold. Currently PGA is defined as …

Due to the growing need for large quantities of human animation data in the entertainment
industry, it has become a necessity to compress motion capture sequences in order to ease …

Principal component analysis (PCA) along with its extensions to manifolds and outlier
contaminated data have been indispensable in computer vision and machine learning. In …

Euclidean statistics are often generalized to Riemannian manifolds by replacing straight-line
interpolations with geodesic ones. While these Riemannian models are familiar-looking …

We present a novel approach for learning a finite mixture model on a Riemannian manifold
in which Euclidean metrics are not applicable and one needs to resort to geodesic distances …

Media streaming is the killer application in current Internet. There are a variety of media
streaming techniques in today's Internet, such as RTSP, HTTP live streaming and Adobe …

In fields ranging from computer vision to signal processing and statistics, increasing
computational power allows a move from classical linear models to models that incorporate …