595 Seminar: Models from and for Computer Vision Fall Quarter 2002

In the "Models for and from Computer Vision" seminar you will get a representative sample of various models used in computer vision. We will discuss what types of models are used to aid computer vision in recognition and tracking; and we will look at various models to describe objects and scenes observed with a camera. The seminar is open for everyone to audit. Signing up for credit means you will give a presentation about one of the topics, oriented on one or two research paper(s). Hurry if you want to sign up: there are only 9 slots for presentations, so the enrollment is limited to 9 students as well.

Schedule

Snakes
M. Kass, A. Witkin and D. Terzopoulos, Snakes: Active Contour Models, First International Conference on Computer Vision, 1987, pp. 259-268.
(also in M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models. Int. J. of Comp. Vision, 1(4), 321-331, 1988.)
Daniel Cremers and Christoph Schnörr and Joachim Weickert, Diffusion-Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework IEEE Workshop on Variational and Levelset Methods, 2001.

Modeling with Principal Component Analysis
These are surface (texture) and/or shape models based on statistical variation within a training set of images.
M. Turk and A. Pentland, Face recognition using eigenfaces, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, 1991.
M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, Winter 1991.
T. F. Cootes and G. J. Edwards and C. J. Taylor, Active Appearance Models, Lecture Notes in Computer Science vol. 1407, 484--??, 1998.
(this might be of interest, too: Christopher J. Taylor Timothy F. Cootes, Gareth J. Edwards. Comparing Active Shape Models with Active Appearance Models. In D.Elliman T.Pridmore, editor, Proceedings of the British Machine Vision Conference, volume 1, pages 173-182, 1999.)

Shape Representation with Level Sets
J. Sethian, Level Set Methods: An Act of Violence, American Scientist 85 (3), May-June 1997. same in pdf. Note that these documents are NOT gzipped - you have to get rid of the .gz extension to view them.
everybody also check out this introduction.
Abdol-Reza Mansouri, Region Tracking via Level Set PDEs without Motion Computation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, July 2002.
S. Osher and J. Sethian, Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations, Journal of Computational Physics, 79, 1988, pp. 12-49.
(this might be helpful to see applications: J. Sethian, Level Set Methods and Fast Marching Methods, Cambridge University Press, Second Edition, 1999. )

Merging Snakes and Level Sets
V. Caselles, R. Kimmel, G. Sapiro, Geodesic Active Contours, IJCV 22(1), 61-79, 1997.
And a little shorter version: V. Caselles, R. Kimmel, G. Sapiro, Geodesic Active Contours, Proceedings of the Fifth International Conference on Computer Vision , June 20-23, 1995, 694-699.
An older version that's missing the figures is available here: citeseer: ps.gz, pdf.
N.Paragios and R.Deriche, Geodesic Active Regions for Motion Estimation and Tracking, In Proceedings of 7th IEEE International Conference in Computer Vision, Greece, 1999.

Superquadric Models
Lin Zhou and Chandra Kambhamettu, Extending Superquadrics with Exponent Functions: Modeling and Reconstruction. IEEE Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, June 1999.
E. Bardinet, L.D. Cohen and N. Ayache, Fitting of Iso-Surfaces Using Superquadrics and Free-Form Deformations IEEE Workshop on Biomedical Image Analysis, 1994.

Modeling Anatomy and Kinematics
John Lin and Ying Wu and Thomas S. Huang, Modeling the Constraints of Human Hand Motion Proceedings of the 5th Annual Federated Laboratory Symposium, 2001.
Lee, J., Kunii, T.L., Model-Based Analysis of Hand Posture. IEEE Computer Graphics and Applications, V. 15, No. 5, pp. 77-86. Sept. 1995.

Image-Based Image Retrieval
Please see Gilad's web page for this week's papers and much more.

Stereo Matching
Stereo matching algorithms try to find depth information in two or more frames of the same scene, shot from different angles.
He-Ping Pan: General Stereo Image Matching Using Symmetric Complex Wavelets presented at SPIE Conference: Wavelet Applications in Signal and Image Processing, VI. Denver, August 1996, Published in SPIE Proceedings, vol. 2825.
D. Scharstein, R. Szeliski, and R. Zabih, A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, (10 pages), Workshop on Stereo and Multi-Baseline Vision (in conjunction with IEEE CVPR 2001), pages 131-140, Kauai, Hawaii, December 2001.
D. Scharstein and R. Szeliski, A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, same in color and two columns, (36 pages), Int. J. on Computer Vision 47(1/2/3):7-42, April-June 2002. Also as Microsoft Research Technical Report MSR-TR-2001-81, November 2001.
Algorithms can be found at this web page: stereo matching algorithms.

Image-based Rendering
Most of the modeling for IBR happens at algorithm construction time: A model of the camera location and motion in respect to the image scene are the main ingredients to cook up new views.
Richard Szeliski and Heung-Yeung Shum, Creating Full View Panoramic Image Mosaics and Environment Maps Proceedings of the 24th annual conference on Computer graphics and interactive techniques, 1997, 251-258.
Heung-Yeung Shum and Adam Kalai and Steven M. Seitz, Omnivergent Stereo, Proc. Seventh International Conference on Computer Vision, 1999.

Mathias Kölsch