ECE 904 Computer Vision Seminar

Fall 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	8/24	Helmut Grabner, Jiri Matas, Luc Van Gool, Philippe Cattin.  Tracking the Invisible: Learning Where the Object Might Be, CVPR 2010.  video	Stan Birchfield
2	8/31	Charles Bibby and Ian Reid, Real-time Tracking of Multiple Occluding Objects using Level Sets, CVPR 2010 (CVPR 2010 poster videos)	Stan Birchfield
3	9/7	Antonio Torralba, "How many pixels make an image?", Visual Neuroscience, 2010	Vidya Murali
4	9/14	S. Stalder, H. Grabner, and L. Van Gool, Beyond Semi-Supervised Tracking: Tracking Should Be as Simple as Detection, but not Simpler than Recognition, In Proceedings ICCV’09 WS on On-line Learning for Computer Vision, 2009.	Sumod Mohan
5	9/21	Bangpeng Yao and Li Fei-Fei, Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2010.	Ninad Pradhan
6	9/28	Hui Kong, Jean-Yves Audibert, and Jean Ponce.  General Road Detection from A Single Image.  IEEE Transactions on Image Processing, Aug. 2010.	Yinxiao Li
7	10/5	Salzmann and Urtasun Combining Discriminative and Generative Methods for 3D Deformable Surface and Articulated Pose Reconstruction  CVPR 2010.  video	(Bryan Willimon)
8	10/12	Y. Li and S. Birchfield.  Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot, IROS 2010. B. Willimon, S. Birchfield, and I. Walker.  Rigid and Non-Rigid Classification Using Interactive Perception, IROS 2010.	Bryan Willimon / Yinxiao Li
9	10/19	[out of town]
10	10/26	Delage, E.  Honglak Lee  Ng, A.Y.  A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image, CVPR 2006	Satyajeet Bhide
11	11/2	[break]
12	11/9	Steve Gu and Ying Zheng and Carlo Tomasi.  Critical Nets and Beta-Stable Features for Image Matching, ECCV 2010.	Salil Banerjee
13	11/16	Georg Klein and David Murray, Parallel Tracking and Mapping for Small AR Workspaces, ISMAR 2007	Brian Peasley
14	11/23	Jianguo Li, Eric Li, Yurong Chen, Li Xu, Yimin Zhang, Bundled Depth-Map Merging for Multi-View Stereo, CVPR 2010.  Video -- Dataset/Results -- Slides	Bryan Willimon
15	11/30	Yiyan Wang, Yuexian Zou, Hang Shi, He Zhao.  Video Image Vehicle Detection System for Signaled Traffic Intersection.  HIS 2009.	Nick Watts

Papers covered in previous semesters

Potential future papers

1. Huang, Guestrin, and Guibas, Fourier Theoretic Probabilistic Inference over Permutations, JMLR 2009

2. Dense Point Trajectories by GPU-accelerated Large Displacement Optical Flow, http://www.eecs.berkeley.edu/Pubs/TechRpts/2010/EECS-2010-104.pdf

3. Matthias Grundmann, Vivek Kwatra, Mei Han, and Irfan Essa, “Efficient Hierarchical Graph-Based Video Segmentation”, CVPR 2010.

4. Ying Nian Wu, Zhangzhang Si, Chuck Fleming, and Song-Chun Zhu, Deformable Template As Active Basis, ICCV 2007

5. Wu and Nevatia. "Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors." IJCV 2007.

6. Imran N. Junejo, Emilie Dexter, Ivan Laptev and Patrick Perez, Cross-View Action Recognition from Temporal
   Self-Similarities, ECCV 2008
7. SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments  Robotics and Autonomous Systems
   Volume 58, Issue 2, 2010.
8. 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.
9. R. B. Rusu, N. Blodow, and M. Beetz, ”Fast Point Feature Histograms (FPFH) for 3D Registration,”
   in ICRA 2009
10. 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.
11. Hiroshi Ishikawa, Higher-Order Clique Reduction in Binary Graph Cut, CVPR 2009
12. O. Juan and Y. Boykov, Active Graph Cuts, CVPR 2006
13. Carsten Rother, Vladimir Kolmogorov, Andrew Blake.  “GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts, SIGGRAPH 2004.
14. Georg Klein and David Murray.  Improving the Agility of Keyframe-based SLAM.  In Proc. European Conference on Computer Vision ECCV'08, 2008
15. 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
16. 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.
17. D. A. Ross et al., Incremental Learning for Robust Visual Tracking, IJCV 2008
18. 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
19. A. Rahimi, L.-P. Morency, and T. Darrell, Reducing Drift in Differential Tracking, Computer Vision and Image Understanding, 109(2):97-111, February 2008
20. 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)  
21. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In Proceedings European Conference on Computer Vision (ECCV), 2008.
22. Komodakis, N. Tziritas, G.  Approximate Labeling via Graph Cuts Based on Linear Programming, PAMI 2007
23. A. Goldberg, M. Li, and X. Zhu. Online Manifold Regularization: A New Learning Setting and Empirical Study. ECML PKDD 2008.
24. E. Royer et al., Monocular Vision for Mobile Robot Localization and Autonomous Navigation, IJCV 2007
25. G. Guo and C. R. Dyer, Patch-based Image Correlation with Rapid Filtering, CVPR 2007
26. 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
27. 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
28. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
29. 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
30. 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.
31. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
32. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
33. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
34. 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.
35. 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.
36. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
37. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
38. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
39. 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.
40. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
41. 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
42. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
43. 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)
44. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
45. S. Avidan, Support vector tracking, CVPR 2001
46. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
47. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
48. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
49. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
50. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
51. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
52. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
53. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
54. Manifold learning web page
55. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
56. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
57. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
58. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
59. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
60. Yann, LeNet-5 convolutional neural networks -- homepage
61. 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.
62. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
63. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
64. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
65. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
66. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
67. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
68. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
69. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
70. 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. 
71. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
72. 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.)