ECE 904 Computer Vision Seminar

Fall 2009

In this course we review recent research publications related to visual detection, recognition, and tracking of people (or other objects), visual motion analysis, visual reconstruction, stereo vision, acoustic localization, robotic sensing, and other related topics. Each week we meet to discuss one paper from the recent literature.  Students should read the paper beforehand and prepare questions and comments in order to participate fully in the discussion.  In addition, students are encouraged to volunteer to lead the discussion at least once during the semester.  All students are welcome to attend, whether or not they are signed up for the course. (For details on how to get credit, see the bottom of this page.)

Here are some miscellaneous computer vision resources.

Schedule

Week	Date	Paper	Discussion leader
1	8/25	Hiroshi Ishikawa, Exact Optimization for Markov Random Fields with Convex Priors, PAMI 2003	Stan Birchfield
2	9/1	S. Roy, Stereo Without Epipolar Lines: A Maximum-Flow Formulation, IJCV 1999
3	9/8	Y. Boykov, O. Veksler, and R. Zabih. Markov random fields with efficient approximations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 648–655, 1998.	Salil Banerjee
4	9/15	Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. In Proceedings of the International Conference on Computer Vision, pages 377–384, 1999.	Ninad Pradhan
5	9/22	Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222–1239, 2001.	Xiaoxia Huang
6	9/29	[out of town]
7	10/6	V. Kolmogorov and R. Zabih. Multi-camera scene reconstruction via graph cuts. In Proceedings of the European Conference on Computer Vision, 2002.	Bryan Willimon
8	10/13	[break]
9	10/20	V. Kolmogorov and R. Zabih. What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2):147–159, 2004.	Sumod Mohan
10	10/27	Zia Khan, Tucker Balch, and Frank Dellaert, MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets, PAMI 2005	Akshay Apte
11	11/3	Z. Tu and S. Zhu, Image Segmentation by Data-Driven Markov Chain Monte Carlo, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):657-673, 2002.	Zhichao Chen
12	11/10	R. Zabih and V. Kolmogorov. Spatially coherent clustering with graph cuts. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004.	Vidya Murali
13	11/17	Y. Boykov and V. Kolmogorov.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, PAMI 2004	Stan Birchfield
14	11/24	P. Kohli and P. H. S. Torr.  Dynamic Graph Cuts for Efficient Inference in Markov Random Fields, PAMI, 29(12):  2079-2088, December 2007.	Xiaoxia Huang
15	12/1	Y. Y. Boykov and M.-P. Jolly Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, ICCV 2001

Papers covered in previous semesters

Potential future papers

1. Carsten Rother, Vladimir Kolmogorov, Andrew Blake.  “GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts, SIGGRAPH 2004.
2. Georg Klein and David Murray.  Improving the Agility of Keyframe-based SLAM.  In Proc. European Conference on Computer Vision ECCV'08, 2008
3. Aurélie Bugeau and Patrick Pérezza, Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts Journal on Image and Video Processing, 2008
4. H. Murase and S. K. Nayar, "Visual Learning and Recognition of 3D Objects from Appearance," International Journal of Computer Vision, Vol. 14, No. 1, pp. 5-24, 1995.
5. D. A. Ross et al., Incremental Learning for Robust Visual Tracking, IJCV 2008
6. Emmanuel Candès and Michael Wakin, An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), pp. 21 - 30, March 2008
   1. Richard Baraniuk, Justin Romberg, and Michael Wakin, Tutorial on compressive sensing (2008 Information Theory and Applications Workshop)
   2. Compressive Sensing Resources
7. A. Rahimi, L.-P. Morency, and T. Darrell, Reducing Drift in Differential Tracking, Computer Vision and Image Understanding, 109(2):97-111, February 2008
8. Wagner Daniel, Reitmayr Gerhard, Mulloni Alessandro, Drummond Tom, Schmalstieg Dieter, Pose Tracking from Natural Features on Mobile Phones, The 7th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2008)  
9. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In Proceedings European Conference on Computer Vision (ECCV), 2008.
10. Komodakis, N. Tziritas, G.  Approximate Labeling via Graph Cuts Based on Linear Programming, PAMI 2007
11. A. Goldberg, M. Li, and X. Zhu. Online Manifold Regularization: A New Learning Setting and Empirical Study. ECML PKDD 2008.
12. E. Royer et al., Monocular Vision for Mobile Robot Localization and Autonomous Navigation, IJCV 2007
13. G. Guo and C. R. Dyer, Patch-based Image Correlation with Rapid Filtering, CVPR 2007
14. Denis McCarthy and Frank Boland, A Method for Source-Microphone Range Estimation in Reverberant Environments Using Arrays of Unknown Geometry, EURASIP Journal on Advances in Signal Processing, 2008
15. Willert, V.; Eggert, J.; Adamy, J.; Stahl, R.; Korner, E., A Probabilistic Model for Binaural Sound Localization, IEEE Trans. on Systems, Man, and Cybernetics B, 36(5): 982-994, 2006
16. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
17. Arthur E.C. Pece, Anthony D. Worrall, A comparison between feature-based and EM-based contour tracking, Image and Vision Computing, 24(12): 1218-1232, 2006
18. Sun et al., "Bi-directional Tracking using Trajectory Segment Analysis", ICCV 2005.
19. T.-J. Cham and J. M. Rehg, A Multiple Hypothesis Approach to Figure Tracking, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 239–245, Ft. Collins, CO, June 1999.
20. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
21. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
22. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
23. P. Bouthemy, "A Maximum Likelihood Framework for Determining Moving Edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11,  no. 5,  pp. 499-511,  May 1989.
24. J. Sivic, B. Russell, A.A. Efros, A. Zisserman, and B. Freeman, Discovering Objects and Their Location in Images,
    International Conference on Computer Vision (ICCV 2005), October, 2005.
25. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
26. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
27. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
28. F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce.
    Segmenting, Modeling, and Matching Video Clips Containing Multiple Moving Objects.
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June
    2004, vol. 2, pp. 914-921.
29. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
30. A. M. Martinez and M. Zhu, Where Are Linear Feature Extraction Methods Applicable?, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, Issue 12, pp. 1934-1944, December 2005
31. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
32. Henele Adams, Sanjiv Singh, and Dennis Strelow. An empirical comparison of methods for image-based motion estimation. IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2002. (PDF)
33. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
34. S. Avidan, Support vector tracking, CVPR 2001
35. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
36. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
37. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
38. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
39. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
40. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
41. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
42. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
43. Manifold learning web page
44. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
45. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
46. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
47. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
48. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
49. Yann, LeNet-5 convolutional neural networks -- homepage
50. Kass, Witkin, and Terzopoulos, Snakes:  Active Contour Models, ICCV 1987
51. Grimson et al., Using adaptive tracking to classify and monitor activities in a site, CVPR 1998
52. M. J. Jones and J. M. Rehg, Statistical Color Models with Application to Skin Detection, Int. J. of Computer Vision, 46(1):81-96, Jan 2002.
53. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
54. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
55. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
56. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
57. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
58. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
59. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
60. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
61. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J., Eigenfaces vs. Fisherfaces: Recognition Using Class-Specific Linear Projection, PAMI(19), No. 7, July 1997, pp. 711-720. 
62. Veksler, Fast Variable Window for Stereo Correspondence using Integral Images, CVPR 2003
63. Sullivan, Blake, Isard, and MacCormick, Object Localization by Bayesian Correlation, ICCV 1999
64. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
65. Jianbo Shi, Serge Belongie, Thomas Leung, Jitendra Malik, Image And Video Segmentation: The Normalized Cut Framework, ICIP 1998
66. Boykov, Veksler, Zabih, Markov Random fields with efficient approximations, CVPR 1998
67. Chafik KERMAD, Christophe COLLEWET, Improving Feature Tracking by Robust Points of Interest Selection,
68. Torresani and Bregler, Space-time tracking, ECCV 2002

Administrivia

Instructor: Stan Birchfield, 207-A Riggs Hall, 656-5912, email: stb at clemson
Meetings: 3:30-4:30 T, 307 Riggs Hall

To receive the 1-hour credit, students must

• sign up for the course at the beginning of the semester
• attend each week
• write a brief summary of each paper covered (five sentences minimum), answering
  • What did the authors do?
  • What are the strengths / weaknesses / potential improvements?
Summaries should be turned in via email to the instructor with a subject line of
    Paper summary: Author
where Author is replaced by the name of the first author of the paper. Non-conforming emails will be returned.  Summaries are due before the beginning of the seminar in which the paper is presented.
• lead the discussion at least twice during the semester. You may select a paper either from the suggested list above or you may find one yourself. Either way, you should notify the instructor and get approval of the paper at least one week before your presentation.
  (Note:  A paper summary is not required for the week that you lead the discussion.)