3rd International Workshop on DIFFerential Geometry in Computer Vision and Machine Learning
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When : 2017-07-21
Where : Honolulu, Hawai
Submission Deadline : 2017-04-10
Categories : Machine Learning , Computational Science
3rd International Workshop on DIFFerential Geometry in Computer Vision and Machine Learning(DIFF-CVML 2017)
July 21, 2017, Honolulu, Hawai
Call for Papers:
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. .
Papers primarily based on (but not limited to) the following topics are welcome: (Topics include but not limited to)
- 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.
IMPORTANT DATES:
- Paper Submission: April 10, 2017
- Paper reviews due: May 8, 2017
- Notification to authors: May 13, 2017
- Camera-ready: May 19, 2017
User Name : Simon
Posted 07-02-2017 on 16:11:51 AEDT
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