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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.

征稿信息

重要日期

2017-04-17
初稿截稿日期

征稿范围

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.

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重要日期
  • 06月21日

    2017

    会议日期

  • 04月17日 2017

    初稿截稿日期

  • 06月21日 2017

    注册截止日期

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