no code implementations • 23 Jan 2021 • Hamid Laga, Marcel Padilla, Ian H. Jermyn, Sebastian Kurtek, Mohammed Bennamoun, Anuj Srivastava
With this formulation, the statistical analysis of 4D surfaces can be cast as the problem of analyzing trajectories embedded in a nonlinear Riemannian manifold.
no code implementations • ICCV 2021 • Rasha Friji, Hassen Drira, Faten Chaieb, Hamza Kchok, Sebastian Kurtek
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.
no code implementations • 24 Nov 2020 • Racha Friji, Hassen Drira, Faten Chaieb, Sebastian Kurtek, Hamza Kchok
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.
no code implementations • 22 Jan 2019 • Min Ho Cho, Sebastian Kurtek, Steven N. MacEachern
The classification of shapes is of great interest in diverse areas ranging from medical imaging to computer vision and beyond.
no code implementations • 19 Mar 2011 • Anuj Srivastava, Wei Wu, Sebastian Kurtek, Eric Klassen, J. S. Marron
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