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4D Atlas: Statistical Analysis of the Spatiotemporal Variability in Longitudinal 3D Shape Data
With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold.
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition
Deep Learning architectures, albeit successful in mostcomputer vision tasks, were designed for data with an un-derlying Euclidean structure, which is not usually fulfilledsince pre-processed data may lie on a non-linear space. In this paper, we propose a geometry aware deep learn-ing approach using rigid and non rigid transformation opti-mization for skeleton-based action recognition.
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition
Deep Learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space.
Aggregated Pairwise Classification of Statistical Shapes
The classification of shapes is of great interest in diverse areas ranging from medical imaging to computer vision and beyond.
Registration of Functional Data Using Fisher-Rao Metric
We introduce a novel geometric framework for separating the phase and the amplitude variability in functional data of the type frequently studied in growth curve analysis.
Statistics Theory Applications Methodology Statistics Theory
