Introduction
Advances in matrix manifolds for computer vision
Spaces and manifolds of shapes in computer vision: An overview
On advances in differential-geometric approaches for 2D and 3D shape analyses and activity recognition
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Fitting smoothing splines to time-indexed, noisy points on nonlinear manifolds
Boosted human re-identification using Riemannian manifolds
Natural metrics and least-committed priors for articulated tracking