Image Canons 2009-2010
Course Description
The purpose of the study group is to go through the most important
(canonical) papers in Image Analysis and Pattern Recognitions to
broaden the education of PhD students of the Image Group at the
Department of Computer Science, University of Copenhagen. For that
purpose, after discussion with the senior members of the Image Group,
18 papers have been selected, covering a broad spectrum in Image
Analysis, and being some of the most cited in the field, as well as of
particular importance for the research performed at the Image
Group. Part of this list may be subject to changes after discussion
with the groups more senior staff.
The course will consist in 1h
weekly discussions around one of the selected papers. To that purpose,
all the enrolled student will read the selected paper prior to
discussion, while one of them will present it in details to the
audience. A VIP with deeper knowledge of the paper and techniques
involved will lead the discussion.
PhD students from other
groups as well as non PhD students are welcome too.
Registration
So far, just send a mail to François Lauze: francois at diku dot dk
Schedule
When: Mondays 10:30-11:30am
Place: Mødelokalet B, DIKU
Dates may change!
Participants
- Senior speakers
- Mads Nielsen
- Søren Ingvor Olsen
- Jon Sporring
- François Lauze
- Kim Steenstrup Pedersen
- Sune Darkner
- Junior participants
The 18 selected papers
- D. Mumford. Bayesian Rationale for the Variational Formulation.
In Geometry-Driven Diffusion In Computer Vision, pages 135-146.
Kluwer Academic Publishers, 1994. Computational Imaging And Vision.
- S. Geman and D. Geman. Stochastic Relaxation, Gibbs Distributions,
and the Bayesian Restoration of Images.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
6(6): 721-741, 1984.
- T. F. Cootes, C. Taylor, D. Cooper, and J. Graham. Active Shape Models-their
Training and Application. Computer Vision and Image Understanding,
61(1):38-59, 1995.
- V. Caselles, R. Kimmel, and G. Sapiro. Geodesic Active Contours.
The International Journal of Computer Vision, 22(1): 61-79, 1997.
They were proposed simultaneously by S. Kichenassamy, A. Kumar, P. J Olver, A. Tannenbaum, A. Yezzi.
Conformal curvature flows: From phase transitions to active vision,
Archives for Rational Mechanics and Analysis 134(3): 275-301, 1996.
- T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active Appearance Models.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
23(6): 681-685, 2001.
- Olivier Faugeras. What Can Be Seen in Three Dimensions with an
Uncalibrated Stereo Rig? European Conference on Computer Vision,
volume 588 of Lecture Notes in Computer Science, pages 563-578, 1992.
- M. H. Hansen and B. Yu. Model Selection and the Principle of Minimum
Description Length. Journal of the American Statistical Association,
96(454): 746-774, 2001.
- B. K. Horn and B. G. Schunck. Determining Optical Flow.
Artificial Intelligence, 17: 185--203, 1981.
- M. Isard and A. Blake. CONDENSATION - Conditional Density Propagation
for Visual Tracking. The International Journal of Computer Vision,
29(1): 5-28, 1998.
- A. K. Jain, R. Duin, and J. Mao. Statistical Pattern Recognition: a Review.
IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(1): 4--38, 2000.
- D. G. Kendall. Shape Manifolds, Procrustean Metrics and Complex
Projective Spaces. Bulletin of London Mathematical Society, 16: 81-121, 1984.
- J. J. Koenderink. The Structure of Images.
Biological Cybernetics, 50: 35-41, 1984.
- T. Lindeberg. Feature Detection With Automatic Scale Selection.
The International Journal of Computer Vision, 30(2): 77-116, 1998.
- P. Perona and J. Malik. Scale-Space and Edge Detection Using
Anisotropic Diffusion. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 12(7): 629-639, July 1990.
- D. Rueckert, L. I. Sonoda, C. Hayes, D. L. G. Hills, M. O. Leach, and D. J. Hawkes.
Nonrigid Registration Using Free-Form Deformation: Application to Breast
MR Images. IEEE Transactions on Medical Imaging, 18(8): 712-721, 1999.
- P. Viola and W. M. Wells III. Alignment by Maximization of Mutual Information.
The International Journal of Computer Vision, 24(2): 137-154, 1997.
- S. C. Zhu, Y. Wu, and D. Mumford. Filters, Random Fields and Maximum
Entropy (FRAME). The International Journal of Computer
Vision, 27(2): 1-20, 1998.
- T. Chan and J. Shen. Mathematical Models for Local Nontexture Inpainting.
SIAM journal of appl. Math, 62(3): 1019-1043, 2002.
About this page
This page is maintained by Chen Chen
and François Lauze. For any question, suggestion... please contact us at:
- chen at diku dot dk
- francois at diku dot dk
Last update: May 3rd 2010.
|