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RFMI 2019 : The IIXth International Workshop on Representation, analysis and recognition of shape and motion FroM Imaging data

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Link: http://www.arts-pi.org.tn/rfmi2019/
 
When Oct 28, 2019 - Oct 31, 2019
Where Hammamet, Tunisia
Submission Deadline May 30, 2019
Notification Due Jun 30, 2019
Final Version Due Jul 30, 2019
Categories    computer vision   pattern recognition   shape analysis   deep learning
 

Call For Papers

The IIXth edition of the international workshop on Representations, analysis and recognition of shape and motion FroM Imaging data (RFMI 2019) will take place in Hammamet (located in the south-eastern section of Cap Bon in Tunisia) from october 28 to 31th. The goal of the workshop is to promote interaction and collaboration among researchers working on static and dynamic shape analysis and their applications in computer vision, pattern recognition, computer graphics and animation, robotics, cultural heritage conservation, medical imaging and healthcare diagnostics. The rapid development of emerging imaging sensors technologies (3D/4D cameras, medical imaging devices, depth-consumer cameras, 3D/4D microscopy, etc.) is pushing forth new research directions to study imaged shapes as well as their motion for advanced modeling, statistical analysis and behavior understanding. The perspective of the workshop will be to strengthen the relationship between the many areas that have as a key meet point, the study of shape and motion and the design of relevant geometric and computational tools. Thus, it will be a great opportunity to encourage links between researchers who share common problems and frequently use similar tools. The fundamental topics of the workshop are (but not limited to),

Computer vision and pattern recognition
n-D shape analysis
G-invariants of a group (local, differential, integral, multi-scale,...) and applications
Shape dynamics assessment
Shape indexation and retrieval
Functional Data Analysis (FDA)
Affective computing
Biometrics recognition
Face and gesture classification and recognition
Biological data modeling and analysis
Biomedical applications of shape analysis and G-invariants
Methods for video-surveillance
Computer graphics and animation
Deep Learning
Geometric Deep Learning

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