ECE 904 Computer Vision Seminar

Spring 2007

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	1/16	F. Porikli, O. Tuzel, and P. Meer, Covariance Tracking using Model Update Based on Lie Algebra, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2006	Stan Birchfield
2	1/23	H. Grabner and H. Bischof, On-line boosting and vision, CVPR 2006	Stan Birchfield
3	1/30	Margrit Betke, Esin Haritaoglu and Larry S. Davis, Real-time multiple vehicle detection and tracking from a moving vehicle, Machine Vision and Applications, 12(2):69-83, September 2000	Guang Zeng
4	2/6	Ramos, F.T., Upcroft, B., Kumar, S. & Durrant-Whyte, H.F., A Bayesian Approach for Place Recognition, IJCAI Workshop on Reasoning with Uncertainty in Robotics (RUR-05), 2005	Vidya Murali
5	2/13	Omar Javed, Saad Ali and Mubarak Shah, Online Detection and Classification of Moving Objects Using Progressively Improving Detectors, CVPR 2005	Neeraj Kanhere
6	2/20	Chang Huang, Haizhou Ai, Yuan Li, and  Shihong Lao, Vector Boosting for Rotation Invariant Multi-View Face Detection, ICCV 2005, pages 446 - 453	Stan Birchfield
7	2/27	Ramos, F.T., Nieto, J.I. and Durrant-Whyte, H.F. 'Recognising and Modelling Landmarks to Close Loops in Outdoor SLAM' In Proceedings IEEE International Conference on Robotics and Automation (ICRA) 2007	Vidya Murali
8	3/6	Radu Stoica, Xavier Descombes and Josiane Zerubia, A Gibbs Point Process for Road Extraction from Remotely Sensed Images. International Journal of Computer Vision, 57(2): 121-136, May 2004	Guang Zeng
9	3/13	Martin Persson, Mats Sandvall, and Tom Duckett, “Automatic building detection from aerial images for mobile robot mapping”, In Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, pages 273-278, June 2005.  (grayscale version)	Vidya Murali
10	3/20	[break]
11	3/27	Todd Schoepflin and Daniel Dailey, Dynamic Camera Calibration of Roadside Management Cameras for Vehicle Speed Estimation, IEEE Transactions on Intelligent Transportation Systems, 4(2), June 2003	Neeraj Kanhere
12	4/3	P. Felzenszwalb and D. Huttenlocher, Efficient belief propagation for early vision, CVPR 2004	Stan Birchfield
13	4/10	N. Vlassis and A. Lika, A Greedy EM Algorithm for Gaussian Mixture Learning, Neural Processing Letters, 2002	Stan Birchfield
14	4/17	Fatih Porikli, Jie Shao and Hide Maehara, "Heli-Tele: Road Extraction from Helicopter Video", Proc IAPR Conf. on Machine Vision Applications, May 2005	Guang Zeng
15	4/24	C. Steger, An Unbiased Detector of Curvilinear Structures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 2, pp. 113-125, 1998. (tech report at citeseer)	Guang Zeng
16

Papers covered in previous semesters

Potential future papers

1. 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
2. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
3. 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
4. Sun et al., "Bi-directional Tracking using Trajectory Segment Analysis", ICCV 2005.
5. 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.
6. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
7. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
8. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
9. 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.
10. Alper Yilmaz, Xin Li, and Mubarak Shah, Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras, PAMI 26(11), 2004
11. 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.
12. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
13. P. Felzenszwalb and D. Huttenlocher, Efficient Belief Propagation for Early Vision, CVPR 2004.
14. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
15. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
16. 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.
17. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
18. 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
19. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
20. 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)
21. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
22. S. Avidan, Support vector tracking, CVPR 2001
23. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
24. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
25. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
26. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
27. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
28. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
29. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
30. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
31. Manifold learning web page
32. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
33. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
34. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
35. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
36. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
37. Yann, LeNet-5 convolutional neural networks -- homepage
38. Kass, Witkin, and Terzopoulos, Snakes:  Active Contour Models, ICCV 1987
39. Plumpe et al., Modeling of the Glottal Flow Derivative Waveform with Application to Speaker Identification, IEEE Transactions on speech and audio processing, 7(5), Sept. 1999
40. Grimson et al., Using adaptive tracking to classify and monitor activities in a site, CVPR 1998
41. 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.
42. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
43. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
44. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
45. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
46. Choo and Fleet.  People tracking using hybrid Monte Carlo filtering, ICCV 2001
47. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
48. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
49. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
50. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
51. 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. 
52. Veksler, Fast Variable Window for Stereo Correspondence using Integral Images, CVPR 2003
53. Sullivan, Blake, Isard, and MacCormick, Object Localization by Bayesian Correlation, ICCV 1999
54. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
55. Jianbo Shi, Serge Belongie, Thomas Leung, Jitendra Malik, Image And Video Segmentation: The Normalized Cut Framework, ICIP 1998
56. Boykov, Veksler, Zabih, Markov Random fields with efficient approximations, CVPR 1998
57. Chafik KERMAD, Christophe COLLEWET, Improving Feature Tracking by Robust Points of Interest Selection,
58. 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 pm Tuesdays, 300 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 15 minutes before the beginning of the seminar.
• 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.)