ECE 904 Computer Vision Seminar

Fall 2008

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	8/26	Thomas Brox, Andres Bruhn, and Joachim Weickert, Variational Motion Segmentation with Level Sets, ECCV 2006	Stan Birchfield
2	9/2	A.B. Ashraf, S. Lucey, and T. Chen, Learning Patch Correspondences for Improved Viewpoint Invariant Face Recognition, CVPR 2008.	Manish Shiralkar
3	9/9	Alper Yilmaz, Xin Li, and Mubarak Shah, Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras, PAMI 26(11), 2004	Wayne Ryan
4	9/16	Dileep George and Jeff Hawkins, A Hierarchical Bayesian Model of Invariant Pattern Recognition in the Visual Cortex, IJCNN 2005	Sumod Mohan
5	9/23	Changki Min and Gerard Medioni, Tensor Voting Accelerated by Graphics Processing Units (GPU), ICPR 2006.	Trupti Patil
6	9/30	A. Sarti, R. Malladi, and J.A. Sethian, Subjective Surfaces: A Geometric Model for Boundary Completion, IJCV 46(3), 201-221, 2002.	Nikhil Rane
7	10/7	N. Vlassis and A. Likas, A Greedy EM Algorithm for Gaussian Mixture Learning, Neural Processing Letters, 2002	Prakash C
8	10/14	B. Daubney, D. Gibson, N. Campbell, Real-time pose estimation of articulated objects using low-level motion, CVPR 2008.	Shrinivas Pundlik
9	10/21	A. Torralba, R. Fergus, and W. T. Freeman, 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, no.11, pp.1958-1970, Nov. 2008	Vidya Murali
10	10/28	Manish P. Shiralkar and Robert J. Schalkoff, Multiple-Class Spatiotemporal Flow Estimation Using a Modified Neural Gas Algorithm, submitted to Optical Engineering	Manish Shiralkar
11	11/4	[break]
12	11/11	W. J. Ryan, D. L. Woodard, A. T. Duchowski, and S. T. Birchfield, Adapting Starburst for Elliptical Iris Segmentation, Proceedings of the IEEE Second International Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, D.C., September 2008.	Wayne Ryan
13	11/18	A. Adam, E. Rivlin, and I. Shimshoni, Robust Fragments-based Tracking using the Integral Histogram, Computer Vision and Pattern Recognition, 2006	Nalin Pradeep
14	11/25	Ashutosh Saxena, Min Sun, Andrew Y. Ng, Make3D: Learning 3-D Scene Structure from a Single Still Image, To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2008.	Sumod Mohan
15	12/2	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	Nikhil Rane

Papers covered in previous semesters

Potential future papers

1. Pedro F. Felzenszwalb and Daniel P. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, Volume 59, Number 2, September 2004.
2. E. Royer et al., Monocular Vision for Mobile Robot Localization and Autonomous Navigation, IJCV 2007
3. G. Guo and C. R. Dyer, Patch-based Image Correlation with Rapid Filtering, CVPR 2007
4. G. J. Brostow, R. Cipolla, Unsupervised Bayesian Detection of Independent Motion in Crowds, CVPR 2006.
5. 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
6. 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
7. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
8. 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
9. Sun et al., "Bi-directional Tracking using Trajectory Segment Analysis", ICCV 2005.
10. 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.
11. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
12. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
13. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
14. 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.
15. 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.
16. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
17. P. Felzenszwalb and D. Huttenlocher, Efficient Belief Propagation for Early Vision, CVPR 2004.
18. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
19. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
20. 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.
21. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
22. 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
23. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
24. 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)
25. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
26. S. Avidan, Support vector tracking, CVPR 2001
27. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
28. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
29. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
30. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
31. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
32. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
33. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
34. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
35. Manifold learning web page
36. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
37. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
38. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
39. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
40. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
41. Yann, LeNet-5 convolutional neural networks -- homepage
42. Kass, Witkin, and Terzopoulos, Snakes:  Active Contour Models, ICCV 1987
43. Grimson et al., Using adaptive tracking to classify and monitor activities in a site, CVPR 1998
44. 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.
45. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
46. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
47. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
48. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
49. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
50. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
51. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
52. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
53. 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. 
54. Veksler, Fast Variable Window for Stereo Correspondence using Integral Images, CVPR 2003
55. Sullivan, Blake, Isard, and MacCormick, Object Localization by Bayesian Correlation, ICCV 1999
56. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
57. Jianbo Shi, Serge Belongie, Thomas Leung, Jitendra Malik, Image And Video Segmentation: The Normalized Cut Framework, ICIP 1998
58. Boykov, Veksler, Zabih, Markov Random fields with efficient approximations, CVPR 1998
59. Chafik KERMAD, Christophe COLLEWET, Improving Feature Tracking by Robust Points of Interest Selection,
60. 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.)