ECE 904 Computer Vision Seminar
Fall 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



Thomas Whelan et al., Kintinuous: Spatially Extended KinectFusion, 2012 Stan Birchfield



Jose M. Alvarez, Theo Gevers, and Antonio M. Lopez, 3D Scene Priors for Road Detection, CVPR 2010 Doug Dawson



Gunhee Kim and Eric P. Xing, On Multiple Foreground Cosegmentation, CVPR 2012 Rahul Suresh






Mohammed Benjelloun, Saïd Mahmoudi, and Fabian Lecron, A Framework of Vertebra Segmentation Using the Active Shape Model-Based Approach, International Journal of Biomedical Imaging, 2011 Satish Ravindran



A. Plotnik and S. Rock, Hybrid Estimation Using Perceptional Information: Robotic Tracking of Deep Ocean Animals, IEEE Journal of Oceanic Engineering, 2011   Sukrat



[out of town]  



[fall break]  



Aquino et al., Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques, IEEE Trans. on Medical Imaging, 2010 Poornapragna



L. Bo, X. Ren, and D. Fox. Depth Kernel Descriptors for Object Recognition Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011   Surya



D. Leng and W. Sun, Finding All The Solutions of PnP Problem, International Workshop on Imaging Systems and Techniques, 2009   JP Kwon



 [out of town]  



 [out of town]  



Hema S. Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena. Semantic Labeling of 3D Point Clouds for Indoor Scenes. In NIPS, 2011.    Kalaivani
15 12/3 Bin Fan, Fuchao Wu and Zhanyi Hu. Line matching leveraged by point correspondences, CVPR 2010.    Xiaoxia

Papers covered in previous semesters

Potential future papers

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