ECE 904 Computer Vision Seminar

Spring 2010

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	1/12	[out of town]
2	1/19	Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov, Neighbourhood Component Analysis, Neural Information Processing Systems 17 (NIPS'04). pp. 513-520.	Sumod Mohan
3	1/26	Simei Gomes Wysoski, Lubica Benuskovaa and Nikola Kasabova.  Fast and adaptive network of spiking neurons for multi-view visual pattern recognition.  Neurocomputing, Volume 71, Issues 13-15, August 2008, Pages 2563-2575	Kenneth Rice
4	2/2	M. F. Tappen and W. T. Freeman. “Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters”. In Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV), Pages 900 - 907, 2003	Sumod Mohan
5	2/9	Jian Sun, Weiwei Zhang, Xiaoou Tang, and Heung-Yeung Shum, Bi-directional Tracking using Trajectory Segment Analysis, ICCV 2005.	Yujie Dong
6	2/16	R. Fergus, Y. Weiss, and A. Torralba, Semi-supervised Learning in Gigantic Image Collections, Advances in Neural Information Processing Systems, 2009.	Vidya Murali
7	2/23	L. Torresani and C. Bregler, Space-time tracking, ECCV 2002	Nikhil Rane
8	3/2	Charles Bibby and Ian Reid, Robust Real-Time Visual Tracking using Pixel-Wise Posteriors, ECCV 2008	Brian Peasley
9	3/9	K Schindler, L Van Gool, Action Snippets : How many frames does human action recognition require?, CVPR 2008	Ninad Pradhan
10	3/16	[break]
11	3/23	H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000.	Eric Fang
12	3/30	Imran Saleemi, Khurram Shafique, and Mubarak Shah.  Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), Aug 2009	Salil Banerjee
13	4/6	Massimiliano Pontil and Alessandro Verri.  Support Vector Machines for 3D Object Recognition.  PAMI 1998	Bryan Willimon
14	4/13	Noah Snavely, Steven M. Seitz, Richard Szeliski.  Photo Tourism: Exploring Photo Collections in 3D.  SIGGRAPH 2006.	Xiaoxia Huang
15	4/20

Papers covered in previous semesters

Potential future papers

1. R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz, ”Towards 3D Point Cloud Based
   Object Maps for Household Environments,” Robotics and Autonomous Systems Journal (Special
   Issue on Semantic Knowledge), 2008.
2. R. B. Rusu, N. Blodow, and M. Beetz, ”Fast Point Feature Histograms (FPFH) for 3D Registration,”
   in ICRA 2009
3. Yuri Boykov, Gareth Funka-Lea.  Graph Cuts and Efficient N-D Image Segmentation. In International Journal of Computer Vision, vol. 70, no. 2, pp. 109-131, 2006.
4. Hiroshi Ishikawa, Higher-Order Clique Reduction in Binary Graph Cut, CVPR 2009
5. O. Juan and Y. Boykov, Active Graph Cuts, CVPR 2006
6. Carsten Rother, Vladimir Kolmogorov, Andrew Blake.  “GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts, SIGGRAPH 2004.
7. Georg Klein and David Murray.  Improving the Agility of Keyframe-based SLAM.  In Proc. European Conference on Computer Vision ECCV'08, 2008
8. 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
9. 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.
10. D. A. Ross et al., Incremental Learning for Robust Visual Tracking, IJCV 2008
11. 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
12. A. Rahimi, L.-P. Morency, and T. Darrell, Reducing Drift in Differential Tracking, Computer Vision and Image Understanding, 109(2):97-111, February 2008
13. 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)  
14. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In Proceedings European Conference on Computer Vision (ECCV), 2008.
15. Komodakis, N. Tziritas, G.  Approximate Labeling via Graph Cuts Based on Linear Programming, PAMI 2007
16. A. Goldberg, M. Li, and X. Zhu. Online Manifold Regularization: A New Learning Setting and Empirical Study. ECML PKDD 2008.
17. E. Royer et al., Monocular Vision for Mobile Robot Localization and Autonomous Navigation, IJCV 2007
18. G. Guo and C. R. Dyer, Patch-based Image Correlation with Rapid Filtering, CVPR 2007
19. 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
20. 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
21. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
22. 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
23. 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.
24. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
25. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
26. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
27. 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.
28. 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.
29. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
30. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
31. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
32. 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.
33. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
34. 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
35. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
36. 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)
37. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
38. S. Avidan, Support vector tracking, CVPR 2001
39. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
40. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
41. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
42. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
43. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
44. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
45. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
46. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
47. Manifold learning web page
48. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
49. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
50. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
51. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
52. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
53. Yann, LeNet-5 convolutional neural networks -- homepage
54. 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.
55. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
56. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
57. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
58. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
59. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
60. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
61. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
62. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
63. 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. 
64. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
65. Chafik KERMAD, Christophe COLLEWET, Improving Feature Tracking by Robust Points of Interest Selection,

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
    ECE 904 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 once 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.  Be sure not to request to present a paper that has already been presented in previous semesters (see link above).
  (Note:  A paper summary is not required for the week that you lead the discussion.)