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Workshop Focus and Aims
- 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.
Important dates
Main topics of interest
- . Shape Representations: Silhouettes, Surfaces, Skeletons, Humans, etc..
- . Information Geometry: Fisher-Rao and elastic metrics, Gromov-Wasserstein family, Earth-Mover’s distance, etc.
- . Dynamical Systems: Trajectories on manifolds, Rate-invariance, Identification and classification of systems.
- . Domain Transfer: Ideas and applications.
- . Image/Volume/Trajectory: Spatial and temporal registration & segmentation.
- . Manifold-Valued Features: Histograms, Covariance, Symmetric positive-definite matrices, Mixture model.
- . Big Data: Dimension-reduction using geometric tools.
- . Bayesian Inferences: Nonlinear domains, Computational solutions using differential geometry, Variational approaches.
- . Machine Learning Approaches on Nonlinear Feature Spaces: Kernel methods, Boosting, SVM-type classification, Detection and tracking algorithms.
- . Functional Data Analysis: Hilbert manifolds, Visualization.
- . Applications: Medical analysis, Biometrics, Biology, Environmetrics, Graphics, Activity recognition, Bioinformatics, Pattern recognition, etc.
- . Geometry of articulated bodies: Applications to robotics, biomechanics, and motor control.
- . Computational topology and applications.
Latest News
The Embassy of France in the United States; Office for Science and Technology is sponsoring best-paper prizes
Diff-CVML 2017 Workshop is accepted to be held in conjunction with CVPR 2017 in Honolulu, Hawai.
