Sparsity-based representations have recently led to notable results in various visual
recognition tasks. In a separate line of research, Riemannian manifolds have been shown …

Suitable shape representations as well as their temporal evolution, termed trajectories, often
lie to non-linear manifolds. This puts an additional constraint (ie, non-linearity) in using …

The detection and tracking of human landmarks in video streams has gained in reliability
partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying …

We address the problem of segmenting high angular resolution diffusion imaging (HARDI)
data into multiple regions (or fiber tracts) with distinct diffusion properties. We use the …

In this paper, we present a novel dictionary learning framework for data lying on the manifold
of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields …

In this work, we discuss the higher-order tensors appearing in high angular resolution
diffusion tensor imaging, and we have tested two segmentation methods, the Riemannian …

Statistical models for manifold-valued data permit capturing the intrinsic nature of the curved
spaces in which the data lie and have been a topic of research for several decades …

Since its inception, DTI modality has become an essential tool in the clinical scenario. In
principle, it is rooted in the emergence of symmetric positive definite (SPD) second-order …

Crime and terrorism in the 21st century call for advancement in the modeling and simulation
of criminal events in the complex environment. This presentation reviews the field of …

Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive technique to
probe the complex micro-architecture of the tissue being imaged. The diffusional properties …