ECE 904 Computer Vision Seminar

Spring 2011

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	1/17	[break]
2	1/24	[out of town]
3	1/31	R. B. Rusu, N. Blodow, and M. Beetz, "Fast Point Feature Histograms (FPFH) for 3D Registration,” ICRA 2009	Stan Birchfield
4	2/7	Tilke Judd, Frédo Durand, Antonio Torralba, Fixations on Low Resolution Images, Journal Of Vision, Aug 2010	Vidya Murali
5	2/14	Schweitzer, M.; Wuensche, H.-J.  Efficient keypoint matching for robot vision using GPUs, ICCV workshop 2009	Gauri Phatak
6	2/21	C. Valgren and Z. J. Lilienthal.  SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments  Robotics and Autonomous Systems, Volume 58, Issue 2, 2010.	Ryan Bontreger
7	2/28	Wang, Wu, and Hu.  MSLD: A robust descriptor for line matching.  Pattern Recognition, 42(5), 2009	Xiaoxia Huang
8	3/7	A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001	Shyam Sundar
9	3/14	J. Kwon and K. M. Lee. Visual tracking decomposition.  Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.	Rahul Suresh
10	3/21	[spring break]
11	3/28	Pathan, S.S.; Al-Hamadi, A.; Michaelis, B.  Intelligent Feature-guided Multi-object Tracking Using Kalman Filter.  2nd International Conference on Computer, Control and Communication (IC4 2009), 2009.	Tushar Janefalker
12	4/4	T. Brox, C. Bregler, J. Malik.  Large displacement optical flow.  CVPR 2009.	Kalaivani Sundararajan
13	4/11	T.N. Schoepflin, D.J. Dailey. Dynamic Camera Calibration of Roadside Traffic Management Cameras for Vehicle Speed Estimation. IEEE Transactions on Intelligent Transportation Systems, Volume 4 Issue 2, 2003.	Nick Watts
14	4/18	R. Hartley.  In Defence of the 8-Point algorithm, ICCV 1995.  (journal version)	Brian Peasley
15	4/25	Tai et al.  Super Resolution using Edge Prior and Single Image Detail Synthesis	Satyajeet Bhide

Papers covered in previous semesters

Potential future papers

1. Kuttel et al.,  What's going on? Discovering Spatio-Temporal Dependencies in Dynamic Scenes, CVPR 2010

2. Elad, M.; Aharon, M.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries.  IEEE Transactions onImage Processing, Dec. 2006

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

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

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

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

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

8. Imran N. Junejo, Emilie Dexter, Ivan Laptev and Patrick Perez, Cross-View Action Recognition from Temporal Self-Similarities, ECCV 2008
9. 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.
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. Yann, LeNet-5 convolutional neural networks -- homepage
60. 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.
61. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
62. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
63. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
64. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
65. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
66. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
67. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
68. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
69. 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. 
70. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
71. Chafik KERMAD, Christophe COLLEWET, Improving Feature Tracking by Robust Points of Interest Selection,

Administrivia

Instructor: Stan Birchfield, 209 Riggs Hall, 656-5912, email: stb at clemson
Meetings: 4:00-5:00 M, 219 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.)