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.
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.
06月21日
2017
会议日期
初稿截稿日期
注册截止日期
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