The workshop programme can be downloaded from here.
Diff-CV: Differential Geometry in Computer Vision for Analysis of Shapes, Images and Trajectories
Location: Faraday Lecture Theatre
Workshop website: http://www-rech.telecom-lille.fr/diff-cv2015/
Call for papers: http://www-rech.telecom-lille.fr/diff-cv2015/Call_for_papers.html
Paper submission details: http://www-rech.telecom-lille.fr/diff-cv2015/Submission.html
- Hassen Drira, Institut Mines- Telecom/TELECOM Lille, France
- Sebastian Kurtek, Ohio State University, USA
- Pavan Turaga, Arizona State University, USA
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.
Original papers related to the topics of interest below can be submitted through the workshop webpage. Papers covering theory and/or application areas of computer vision are invited for submission. All papers will be reviewed under the double blind review process. Authors should submit a workshop paper with a minimum of 4 pages and maximum of 9 pages (excluding ￼references). It should follow the same formatting style as a BMVC conference paper.
The topics of interest include, but are not limited to:
- Shape representation
- Riemannian metrics in computer vision
- Curve, surface, trajectory and image registration
- Statistical analysis of shapes, trajectories and images
- Feature-based representations
- Shape detection, tracking and retrieval
- Symmetry analysis
- Applications: medical imaging, graphics, biometrics, activity recognition, bioinformatics,
Keynote Speaker: Dr. Anuj Srivastava, Distinguished Research Professor, Department of Statistics, Florida State University, Tallahassee, FL, USA
Anuj Srivastava is currently a Distinguished Research Professor of Statistics at Florida State University in Tallahassee, FL, USA. He received the Ph.D. degree in Electrical Engineering from the Washington University in St. Louis in 1996. Upon completion, he was a visiting research scientist in the Division of Applied Mathematics at Brown University. Professor Srivastava has an outstanding publication record in the areas of statistics and computer vision including numerous publications in top-tier statistics and IEEE journals as well as highly selective computer vision conferences. He was invited to give the plenary talk at the 2013 International Conference on Image Processing in Melbourne, Australia. He is a Senior Member of the IEEE and an IAPR fellow. He has received numerous awards including several best paper awards at conferences and workshops, the Developing Scholar Award, the Graduate Faculty Mentor Award, and the Fullbright Award. He has received funding from the National Science Foundation, National Institutes of Health, Office of Naval Research, Air Force Office of Scientific Research and Army Research Laboratory. Professor Srivastava’s main research interests lie in the area of statistical image understanding with a focus on fundamental issues. He is well known for his contributions to the field of statistical shape analysis.