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.




Discussion leader






[out of town]  



R. B. Rusu, N. Blodow, and M. Beetz, "Fast Point Feature Histograms (FPFH) for 3D Registration,” ICRA 2009 Stan Birchfield



Tilke Judd, Frédo Durand, Antonio Torralba, Fixations on Low Resolution Images, Journal Of Vision, Aug 2010 Vidya Murali



Schweitzer, M.; Wuensche, H.-J.  Efficient keypoint matching for robot vision using GPUs, ICCV workshop 2009 Gauri Phatak



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



Wang, Wu, and Hu.  MSLD: A robust descriptor for line matching.  Pattern Recognition, 42(5), 2009 Xiaoxia Huang



A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001 Shyam Sundar



J. Kwon and K. M. Lee. Visual tracking decomposition.  Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. Rahul Suresh



[spring break]  



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



T. Brox, C. Bregler, J. Malik.  Large displacement optical flow.  CVPR 2009. Kalaivani Sundararajan



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



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,

  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,


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

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.