ECE 904 Computer Vision Seminar
Spring 2009

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

1/13

 Chunming Li Chenyang Xu Changfeng Gui Fox, M.D.  Level set evolution without re-initialization: a new variational formulation, CVPR 2005 Stan Birchfield

2

1/20

 N. Passat, C. Ronse, J. Baruthio, J.P. Armspach, and C. Maillot, Cerebral Vascular Atlas Generation for Anatomical Knowledge Modeling and Segmentation Purpose, CVPR 2005. Trupti Patil

3

1/27

 Antonio Torralba, Kevin P. Murphy, William T. Freeman and Mark Rubin,
Context-based vision system for place and object recognition, ICCV 2003.		 Vidya Murali

4

2/3

 Antonio Torralba, Kevin P. Murphy, and William T. Freeman, Sharing Visual Features for Multiclass and Multiview Object Detection, PAMI 2007 Sumod Mohan

5

2/10

 Greg Mori, Serge Belongie, and Jitendra Malik, Efficient Shape Matching Using Shape Contexts, IEEE PAMI, 2005.   Nalin Pradeep

6

2/17

 Lecture:  "An Algorithm for Searching an Alternative Hypothesis Space Using a Variation of MAX-SAT", by Dr. Christopher Griffin, Oak Ridge National Laboratory, from 3:30-4:30 in 100-A Riggs  

7

2/24

 Alauddin Bhuiyan, Baikunth Nath, Joselito Chua and Ramamohanarao Kotagiri, Blood Vessel Segmentation from Color Retinal Images Using Unsupervised Texture Classification, IEEE International Conference on Image Processing, 2007 (ICIP 2007) Uttara Thakre

8

3/3

 Pedro F. Felzenszwalb and Daniel P. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, Volume 59, Number 2, September 2004. (off-campus link) Stan Birchfield

9

3/10

 Lukas Zebedin and Joachim Bauer and Konrad F. Karner and Horst Bischof, Fusion of Feature- and Area-Based Information for Urban Buildings Modeling from Aerial Imagery, ECCV 2008 Peng Xu

10

3/17

 [break]  

11

3/24

 [out of town]  

12

3/31

 G. J. Brostow, R. Cipolla, Unsupervised Bayesian Detection of Independent Motion in Crowds, CVPR 2006 Pavan Yalamanchili

13

4/7

 Vedaldi, A. and Soatto, S., Local Features, All Grown Up, CVPR 2006 Prakash C.

14

4/14

 S. Sanjay Gopal and Thomas J. Hebert, Bayesian Pixel Classification Using Spatially Variant Finite Mixtures and the Generalized EM Algorithm, IEEE Transactions on Image Processing, 1998. Shrinivas Pundlik
15 4/21 A.C. Murillo,  J. Kosecka,  J.J. Guerrero, C. Sagues, Visual door detection integrating appearance and shape cues, Robotics and Autonomous Systems, Volume 56 ,  Issue 6  (June 2008) Pages 512-521. Zhichao Chen

Papers covered in previous semesters


Potential future papers

  1. 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
  2. A. Rahimi, L.-P. Morency, and T. Darrell, Reducing Drift in Differential Tracking, Computer Vision and Image Understanding, 109(2):97-111, February 2008
  3. 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)  
  4. H. Grabner, C. Leistner, and H. Bischof. Semi-supervised on-line boosting for robust tracking. In Proceedings European Conference on Computer Vision (ECCV), 2008.
  5. Komodakis, N. Tziritas, G.  Approximate Labeling via Graph Cuts Based on Linear Programming, PAMI 2007
  6. A. Goldberg, M. Li, and X. Zhu. Online Manifold Regularization: A New Learning Setting and Empirical Study. ECML PKDD 2008.
  7. E. Royer et al., Monocular Vision for Mobile Robot Localization and Autonomous Navigation, IJCV 2007
  8. G. Guo and C. R. Dyer, Patch-based Image Correlation with Rapid Filtering, CVPR 2007
  9. G. J. Brostow, R. Cipolla, Unsupervised Bayesian Detection of Independent Motion in Crowds, CVPR 2006.
  10. 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
  11. 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
  12. Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection using reciprocal-polar rectification, Image and Vision Computing, 24(12): 1301–1312, 2006
  13. 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
  14. Sun et al., "Bi-directional Tracking using Trajectory Segment Analysis", ICCV 2005.
  15. 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.
  16. Jean-Yves Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker
  17. Zoran Zivkovic, Ferdinand van der Heijden, Better features to track by estimating the tracking convergence region, ICPR 2002
  18. Eric Marchand, Francois Chaumette.  Features Tracking For Visual Servoing Purpose, 2004
  19. 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.
  20. 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.
  21. D. Beymer and K. Konolige.  Tracking People from a Mobile Platform.  International Symposium on Experimental Robotics, 2002.
  22. A.R. Mansouri, “Region tracking via level set PDEs without motion computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
  23. Yogesh Rathi Namrata Vaswani Allen Tannenbaum Anthony Yezzi, Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects, CVPR 2005
  24. 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.
  25. Sifakis et al., Video Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP Journal on Applied Signal Processing 2002:4, 379–388
  26. 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
  27. J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion using graph cut, CVPR 2004
  28. 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)
  29. A. Barbu, S.C. Zhu, Graph Partition By Swendsen-Wang Cuts, ICCV 2003
  30. S. Avidan, Support vector tracking, CVPR 2001
  31. Black and Jepson, Eigentracking:  Robust matching and tracking of articulated objects using a view-based representation, IJCV, 26(1), 1998
  32. Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for eigentracking, CVPR 2004
  33. Freeman and Roth, Orientation histograms for hand gesture recognition, Workshop on AFGR, 1995
  34. Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV 2002
  35. Y. Wu, Robust visual tracking by integrating multiple cues based on co-inference learning, IJCV, 58(1), 2004
  36. Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV 2000
  37. Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
  38. Tao Zhang, Daniel Freedman, Tracking Objects using Density Matching and Shape Priors, ICCV 2003
  39. Manifold learning web page
  40. Belkin, Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Comptuation, Vol. 15, Issue 6, June 2003
  41. Antonio Torralba Kevin P. Murphy William T. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, CVPR 2004
  42. Baker and Matthews, Lucas-Kanade 20 years on:  A unifying framework, IJCV 56(3):221-255, 2004  webpage
  43. Molton, Davison, and Reid, Parameterisation and probability in image alignment, ACCV 2004.
  44. A. Davison, "3D Simultaneous Localisation and Map-Building Using Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
  45. Yann, LeNet-5 convolutional neural networks -- homepage
  46. Kass, Witkin, and Terzopoulos, Snakes:  Active Contour Models, ICCV 1987
  47. Grimson et al., Using adaptive tracking to classify and monitor activities in a site, CVPR 1998
  48. 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.
  49. Morency, Rahimi, Darrell, Adaptive View-based Appearance Model, CVPR, 2003
  50. M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV 1999
  51. Wu and Huang,  A Co-inference Approach to Robust Visual Tracking
  52. Sigal, Sclaroff, and Athitsos,  Estimation and prediction of evolving color distributions for skin segmentation under varying illumination, CVPR 2000
  53. Elgammal and Davis, Probabilistic framework for segmenting people under occlusion, ICCV 2001
  54. Rui and Chen, Better proposal distributions:  Object tracking using unscented particle filter, CVPR 2001
  55. Toyama and Blake, Probabilistic Tracking in a Metric Space, ICCV 2001
  56. H. Schneiderman, T. Kanade. A Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR 2000
  57. 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. 
  58. Veksler, Fast Variable Window for Stereo Correspondence using Integral Images, CVPR 2003
  59. Sullivan, Blake, Isard, and MacCormick, Object Localization by Bayesian Correlation, ICCV 1999
  60. Javed, Shafique, Shah, A hierarchical approach to robust background subtraction using color and gradient information,
  61. Jianbo Shi, Serge Belongie, Thomas Leung, Jitendra Malik, Image And Video Segmentation: The Normalized Cut Framework, ICIP 1998
  62. Boykov, Veksler, Zabih, Markov Random fields with efficient approximations, CVPR 1998
  63. Chafik KERMAD, Christophe COLLEWET, Improving Feature Tracking by Robust Points of Interest Selection,
  64. 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, 227 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.