ECE 904 Computer Vision Seminar
Spring 2012

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]  



Shu Wang, Huchuan Lu, Fan Yang, Ming-Hsuan Yang.  Superpixel Tracking, ICCV 2011 Stan Birchfield



Sudheendra Vijayanarasimhan, Kristen Grauman, "Efficient Region Search for Object Detection" CVPR 2011 Rahul Suresh



Anton Andriyenko and Konrad Schindler, Multi-target tracking by continuous energy minimization, CVPR 2011 Doug Dawson



Shen et al., Automatic Tag Generation and Ranking for Sensor-rich Outdoor Videos, MM 2011 Nick Watts






T. Xue et al., Symmetric Piecewise Planar Object Reconstruction from a Single Image, CVPR 2011 Xiaoxia Huang



Herve Jegou, Matthijs Douze and Cordelia Schmid.  Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search.  ECCV 2008. Qing Wang



[spring break]  



Stefano Pellegrini, Andreas Ess and Luc Van Gool. Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings, ECCV 2010 Doug Dawson



Sara Vicente, Carsten Rother, Vladimir Kolmogorov, Object Cosegmentation, CVPR 2011
Rahul Suresh



Scott Spurlock and Richard Souvenir, Dynamic Subset Selection for Multi-Camera Tracking, ACMSE 2012 Nick Watts



Felix X. Yu, Rongrong Ji, and Shih-Fu Chang.  Active Query Sensing for Mobile Location Search, ACM Multimedia 2011.  project Qing Wang
15 4/23 Walk and Drouin, Automatic observation for 3D reconstruction of unknown objects using visual servoing, IROS 2010 Katherine Cameron

Papers covered in previous semesters

Potential future papers

  1. Hossam Isack Yuri Boykov, Energy-based Geometric Multi-Model Fitting, (describes PEARL algorithm), International Journal of Computer Vision (IJCV), vol. 97, no. 2, pp. 123-147, April 2012.
  2. J. Fan, Y. Wu, S. Dai, Discriminative spatial attention for robust tracking, ECCV, 2010
  3. T. B. Dinh, N. Vo, and G. Medioni. Context Tracker: Exploring Supporters and Distracters in Unconstrained Environments. CVPR, 2011
  4. M. Yang, Y. Wu, S. Lao. Intelligent Collaborative Tracking by Mining Auxiliary Objects. CVPR 2006.  Also Ming Yang, Ying Wu, and Gang Hua, "Context-aware Visual Tracking", IEEE Trans. on Pattern Recognition and Machine Intelligence (PAMI), Vol.31, No.7, pp.1195-1209, July, 2009.
  5. Kai M. Wurm et al., Hierarchies of Octrees for Efficient 3D Mapping, IROS 2011
  6. Wang et al., Grasping Unknown Objects Based on 3D Model Reconstruction, ICAIM 2005
  7. Huang, Guestrin, and Guibas, Fourier Theoretic Probabilistic Inference over Permutations, JMLR 2009
  8. Dense Point Trajectories by GPU-accelerated Large Displacement Optical Flow
  9. Matthias Grundmann, Vivek Kwatra, Mei Han, and Irfan Essa, "Efficient Hierarchical Graph-Based Video Segmentation", CVPR 2010.
  10. Ying Nian Wu, Zhangzhang Si, Chuck Fleming, and Song-Chun Zhu, Deformable Template As Active Basis, ICCV 2007
  11. Wu and Nevatia. "Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors." IJCV 2007.
  12. Imran N. Junejo, Emilie Dexter, Ivan Laptev and Patrick Perez, Cross-View Action Recognition from Temporal
    Self-Similarities, ECCV 2008
  13. 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.
  14. R. B. Rusu, N. Blodow, and M. Beetz, "Fast Point Feature Histograms (FPFH) for 3D Registration,” ICRA 2009
  15. 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.
  16. Hiroshi Ishikawa, Higher-Order Clique Reduction in Binary Graph Cut, CVPR 2009
  17. O. Juan and Y. Boykov, Active Graph Cuts, CVPR 2006
  18. Carsten Rother, Vladimir Kolmogorov, Andrew Blake.  “GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts, SIGGRAPH 2004.
  19. Georg Klein and David Murray.  Improving the Agility of Keyframe-based SLAM.  In Proc. European Conference on Computer Vision ECCV'08, 2008
  20. 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
  21. 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.
  22. D. A. Ross et al., Incremental Learning for Robust Visual Tracking, IJCV 2008
  23. 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
  24. A. Rahimi, L.-P. Morency, and T. Darrell, Reducing Drift in Differential Tracking, Computer Vision and Image Understanding, 109(2):97-111, February 2008
  25. 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)  
  26. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In Proceedings European Conference on Computer Vision (ECCV), 2008.
  27. Komodakis, N. Tziritas, G.  Approximate Labeling via Graph Cuts Based on Linear Programming, PAMI 2007
  28. A. Goldberg, M. Li, and X. Zhu. Online Manifold Regularization: A New Learning Setting and Empirical Study. ECML PKDD 2008.
  29. E. Royer et al., Monocular Vision for Mobile Robot Localization and Autonomous Navigation, IJCV 2007
  30. G. Guo and C. R. Dyer, Patch-based Image Correlation with Rapid Filtering, CVPR 2007
  31. 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
  32. 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
  33. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
  34. 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
  35. 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.
  36. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
  37. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
  38. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
  39. 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.
  40. 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.
  41. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
  42. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
  43. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
  44. 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.
  45. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
  46. 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
  47. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
  48. 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)
  49. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
  50. S. Avidan, Support vector tracking, CVPR 2001
  51. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
  52. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
  53. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
  54. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
  55. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
  56. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
  57. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
  58. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
  59. Manifold learning web page
  60. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
  61. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
  62. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
  63. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
  64. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
  65. Yann, LeNet-5 convolutional neural networks -- homepage
  66. 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.
  67. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
  68. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
  69. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
  70. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
  71. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
  72. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
  73. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
  74. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
  75. 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. 
  76. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
  77. 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.