About this Research Topic
The goal of this Research Topic is to invite papers that advance both methodological approaches rooted in geometry, as well as application papers that throw light on the utility of these methods for applications of interest in computer vision, robotics, health analytics, and scientific applications. We encourage both theory papers as well as applied papers, and particularly encourage interdisciplinary and collaborative work across disciplines.
Topics of interest include, but are not limited to:
• Deep learning and geometry
• Riemannian methods in computer vision
• Statistical shape analysis: detection, estimation, and inference
• Statistical analysis on manifolds
• Manifold-valued features and learning
• Machine learning on nonlinear manifolds
• Shape detection, tracking, and retrieval
• Topological methods in structure analysis
• Functional Data Analysis: Hilbert manifolds, Visualization
• Applications: medical imaging and analysis, graphics, biometrics, activity recognition, bioinformatics, etc.
Keywords: Differential geometry, topological data analysis, deep learning, time-series modeling
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.