New: Best student paper awards
The best students awards were assigned to Ankita Shukla, (IIIT- Delhi) for her paper Distance Metric Learning by Optimization on the Stiefel Manifold and Adam Duncan (Florida State University) for his paper Geometric Analysis of Axonal Tree Structures.
Workshop Focus and Aims
This workshop will focus on problems and approaches in computer vision that involve Riemannian geometric computing. Recent interest in this area has resulted in Riemannian geometric principles being applied in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion to name a few. Besides being mathematically appealing, 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 due to their successful application in many hard computer vision problems (noted above). Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours).
Topics of interest
- 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.