Call for papers
Download the CfP in PDF from here.
Riemannian geometric computing has received a lot of recent interest in the computer vision community. In particular, Riemannian geometric principles can be applied to a variety of difficult computer vision problems including face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion to name a few. Besides their nice mathematical formulation, Riemannian computations based on the geometry of underlying manifolds are often faster and more stable than their classical counterparts. Over the past few years, the popularity of Riemannian algorithms has increased several-fold. Some of the mathematical entities that benefit from a geometric analysis include rotation matrices, medial representations, subspace comparisons, symmetric positive-definite matrices, function-spaces, and many more. The topics of interest include, but are not limited to:
- Shape representation
- Riemannian metrics in computer vision
- Curve, surface, trajectory and image registration
- Statistical analysis of shapes, trajectories and images
- Feature-based representations
- Shape detection, tracking and retrieval
- Symmetry analysis
- Applications: medical imaging, graphics, biometrics, activity recognition, bioinformatics, etc.