ECE 904 Computer Vision Seminar
Spring 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.





Discussion leader



[out of town]  



Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov, Neighbourhood Component Analysis, Neural Information Processing Systems 17 (NIPS'04). pp. 513-520. Sumod Mohan



Simei Gomes Wysoski, Lubica Benuskovaa and Nikola Kasabova.  Fast and adaptive network of spiking neurons for multi-view visual pattern recognitionNeurocomputing, Volume 71, Issues 13-15, August 2008, Pages 2563-2575
Kenneth Rice



M. F. Tappen and W. T. Freeman. “Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters”. In Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV), Pages 900 - 907, 2003 Sumod Mohan



Jian Sun, Weiwei Zhang, Xiaoou Tang, and Heung-Yeung Shum, Bi-directional Tracking using Trajectory Segment Analysis, ICCV 2005. Yujie Dong



R. Fergus, Y. Weiss, and A. Torralba, Semi-supervised Learning in Gigantic Image Collections, Advances in Neural Information Processing
Systems, 2009.
Vidya Murali



L. Torresani and C. Bregler, Space-time tracking, ECCV 2002 Nikhil Rane



Charles Bibby and Ian Reid, Robust Real-Time Visual Tracking using Pixel-Wise Posteriors, ECCV 2008 Brian Peasley



K Schindler, L Van Gool, Action Snippets : How many frames does human action recognition require?, CVPR 2008 Ninad Pradhan






H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000. Eric Fang



Imran Saleemi, Khurram Shafique, and Mubarak Shah.  Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), Aug 2009 Salil Banerjee



Massimiliano Pontil and Alessandro Verri.  Support Vector Machines for 3D Object Recognition.  PAMI 1998 Bryan Willimon



Noah Snavely, Steven M. Seitz, Richard Szeliski.  Photo Tourism: Exploring Photo Collections in 3D.  SIGGRAPH 2006. 
Xiaoxia Huang
15 4/20    

Papers covered in previous semesters

Potential future papers

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


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

One absence is allowed per semester, as well as three late summaries. (The summaries are checked once per week, so three late summaries could be three separate summaries each of which is one week late, or it could be one summary that is three weeks late, or any combination thereof.)  Any delinquencies beyond the allowed amount will result in grade reduction.