ECE 904 Computer Vision Seminar

Fall 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	8/29	[cancelled]
2	9/5	Popovic et al., A strategy for grasping unknown objects based on co-planarity and colour information, Robotics and Autonomous Systems, 58(5):551-565, May 2010.	Stan Birchfield
3	9/12	Wu et al, "Learning a Rare Event Detection Cascade by Direct Feature Selection", NIPS 2003.  Alper Yilmaz, Object Tracking: A Survey".	Rahul Suresh
4	9/19	[out of town]
5	9/26	Li et al., Learning Behavioural Context, IJCV 2011.	Doug Dawson
6	10/3	Shotton et al., Real-time human pose recognition in parts from single depth images, CVPR 2011	Vikram Bhide
7	10/10	Junseok Kwon, Kyoung Mu Lee, Tracking by Sampling Trackers, ICCV 2011.
8	10/17	[fall break]
9	10/24	Ce Liu and Deqing Sun, A Bayesian Approach to Adaptive Video Super Resolution, CVPR 2011	Qing Wang
10	10/31	Shahram Izadi et al., KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera, ACM Symposium on User Interface Software and Technology 2011.  video	Qing Wang
11	11/7	R. A. Newcombe et al., KinectFusion: Real-Time Dense Surface Mapping and Tracking, ISMAR 2011.	Qing Wang
12	11/14	Nikhil Naikal, Allen Y. Yang, S. Shankar Sastry, "Informative Feature Selection for Object Recognition via Sparse PCA " ICCV 2011	Rahul Suresh
13	11/21	James Philbin, Josef Sivic and Andrew Zisserman.  Geometric Latent Dirichlet Allocation on a Matching Graph for Large-scale Image Datasets, IJCV 2011	Douglas Dawson
14	11/28	Mohammad Amin Sadeghi and Ali Farhadi.  Recognition Using Visual Phrases.  CVPR 2011.	Vikram Bhide
15	12/5

Papers covered in previous semesters

Potential future papers

1. Kai M. Wurm et al., Hierarchies of Octrees for Efficient 3D Mapping, IROS 2011
2. Walk and Drouin, Automatic observation for 3D reconstruction of unknown objects using visual servoing, IROS 2010
3. Wang et al., Grasping Unknown Objects Based on 3D Model Reconstruction, ICAIM 2005
4. Huang, Guestrin, and Guibas, Fourier Theoretic Probabilistic Inference over Permutations, JMLR 2009
5. Dense Point Trajectories by GPU-accelerated Large Displacement Optical Flow
6. Matthias Grundmann, Vivek Kwatra, Mei Han, and Irfan Essa, "Efficient Hierarchical Graph-Based Video Segmentation", CVPR 2010.
7. Ying Nian Wu, Zhangzhang Si, Chuck Fleming, and Song-Chun Zhu, Deformable Template As Active Basis, ICCV 2007
8. Wu and Nevatia. "Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors." IJCV 2007.
9. Imran N. Junejo, Emilie Dexter, Ivan Laptev and Patrick Perez, Cross-View Action Recognition from Temporal
   Self-Similarities, ECCV 2008
10. 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.
11. R. B. Rusu, N. Blodow, and M. Beetz, "Fast Point Feature Histograms (FPFH) for 3D Registration,” ICRA 2009
12. 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.
13. Hiroshi Ishikawa, Higher-Order Clique Reduction in Binary Graph Cut, CVPR 2009
14. O. Juan and Y. Boykov, Active Graph Cuts, CVPR 2006
15. Carsten Rother, Vladimir Kolmogorov, Andrew Blake.  “GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts, SIGGRAPH 2004.
16. Georg Klein and David Murray.  Improving the Agility of Keyframe-based SLAM.  In Proc. European Conference on Computer Vision ECCV'08, 2008
17. 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
18. 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.
19. D. A. Ross et al., Incremental Learning for Robust Visual Tracking, IJCV 2008
20. 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
21. A. Rahimi, L.-P. Morency, and T. Darrell, Reducing Drift in Differential Tracking, Computer Vision and Image Understanding, 109(2):97-111, February 2008
22. 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)  
23. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In Proceedings European Conference on Computer Vision (ECCV), 2008.
24. Komodakis, N. Tziritas, G.  Approximate Labeling via Graph Cuts Based on Linear Programming, PAMI 2007
25. A. Goldberg, M. Li, and X. Zhu. Online Manifold Regularization: A New Learning Setting and Empirical Study. ECML PKDD 2008.
26. E. Royer et al., Monocular Vision for Mobile Robot Localization and Autonomous Navigation, IJCV 2007
27. G. Guo and C. R. Dyer, Patch-based Image Correlation with Rapid Filtering, CVPR 2007
28. 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
29. 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
30. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
31. 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
32. 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.
33. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
34. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
35. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
36. 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.
37. 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.
38. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
39. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
40. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
41. 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.
42. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
43. 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
44. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
45. 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)
46. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
47. S. Avidan, Support vector tracking, CVPR 2001
48. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
49. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
50. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
51. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
52. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
53. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
54. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
55. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
56. Manifold learning web page
57. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
58. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
59. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
60. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
61. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
62. Yann, LeNet-5 convolutional neural networks -- homepage
63. 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.
64. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
65. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
66. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
67. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
68. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
69. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
70. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
71. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
72. 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. 
73. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
74. 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.)