Computer Vision Seminar
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.
[out of town]
Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov,
Component Analysis, Neural Information Processing
Systems 17 (NIPS'04). pp. 513-520.
Simei Gomes Wysoski, Lubica Benuskovaa and Nikola Kasabova.
Fast and adaptive network of spiking neurons for multi-view visual pattern
recognition. Neurocomputing, Volume 71, Issues 13-15, August
2008, Pages 2563-2575
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
Jian Sun, Weiwei Zhang, Xiaoou Tang, and Heung-Yeung Shum, Bi-directional
Tracking using Trajectory Segment Analysis, ICCV 2005.
|R. Fergus, Y. Weiss, and A. Torralba,
Learning in Gigantic Image Collections, Advances in Neural Information Processing
Torresani and C. Bregler,
tracking, ECCV 2002
Charles Bibby and Ian Reid,
Real-Time Visual Tracking using Pixel-Wise Posteriors, ECCV 2008
K Schindler, L Van Gool,
Action Snippets : How many frames does human action recognition require?,
H. Schneiderman, T. Kanade.
Statistical Method for 3D Object Detection Applied to Faces and Cars,
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
Massimiliano Pontil and Alessandro Verri.
Support Vector Machines for 3D Object Recognition. PAMI 1998||
Noah Snavely, Steven M. Seitz, Richard Szeliski.
Tourism: Exploring Photo Collections in 3D. SIGGRAPH 2006.
Papers covered in previous semesters
- 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
Issue on Semantic Knowledge), 2008.
- R. B. Rusu, N. Blodow, and M. Beetz, ”Fast Point Feature Histograms (FPFH)
for 3D Registration,”
in ICRA 2009
- 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.
- Hiroshi Ishikawa,
Higher-Order Clique Reduction in Binary Graph Cut, CVPR 2009
- O. Juan and Y. Boykov,
Active Graph Cuts, CVPR 2006
- Carsten Rother, Vladimir Kolmogorov, Andrew Blake.
“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts,
- Georg Klein and David Murray.
Improving the Agility of Keyframe-based SLAM. In Proc. European
Conference on Computer Vision ECCV'08, 2008
- 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
- 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.
- D. A. Ross et al.,
Learning for Robust Visual Tracking, IJCV 2008
- Emmanuel Candès and Michael Wakin,
An introduction to
compressive sampling. IEEE Signal Processing Magazine, 25(2), pp. 21 - 30,
- Richard Baraniuk, Justin Romberg, and Michael Wakin,
Tutorial on compressive sensing (2008 Information Theory and Applications
- Compressive Sensing Resources
- A. Rahimi, L.-P. Morency, and T. Darrell,
Drift in Differential Tracking, Computer Vision and Image Understanding,
109(2):97-111, February 2008
- Wagner Daniel, Reitmayr Gerhard, Mulloni Alessandro, Drummond Tom,
Tracking from Natural Features on Mobile Phones, The 7th IEEE and ACM
International Symposium on Mixed and Augmented Reality (ISMAR 2008)
- H. Grabner, C. Leistner, and H. Bischof.
Semi-supervised on-line boosting for robust tracking. In Proceedings
European Conference on Computer Vision (ECCV), 2008.
- Komodakis, N. Tziritas, G.
Approximate Labeling via Graph Cuts Based on Linear Programming, PAMI 2007
- A. Goldberg, M. Li, and X. Zhu.
Manifold Regularization: A New Learning Setting and Empirical Study. ECML
- E. Royer et al.,
Monocular Vision for Mobile Robot Localization and Autonomous Navigation,
- G. Guo and C. R. Dyer,
Patch-based Image Correlation with Rapid Filtering, CVPR 2007
- Denis McCarthy and Frank Boland,
Method for Source-Microphone Range Estimation in Reverberant Environments
Using Arrays of Unknown Geometry, EURASIP Journal on Advances in Signal
- 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
- Zezhi Chen, Nick Pears and Bojian Liang, Monocular obstacle detection
using reciprocal-polar rectification, Image and Vision Computing,
24(12): 1301–1312, 2006
- Arthur E.C. Pece, Anthony D. Worrall, A comparison between feature-based
and EM-based contour tracking, Image and Vision Computing, 24(12):
- T.-J. Cham and J. M. Rehg,
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.
- Jean-Yves Bouguet,
Pyramidal Implementation of the Lucas Kanade Feature Tracker
- Zoran Zivkovic, Ferdinand van der Heijden,
Better features to track by estimating the tracking convergence region,
- Eric Marchand, Francois Chaumette.
Features Tracking For Visual Servoing Purpose, 2004
- 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.
- 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.
- D. Beymer and K. Konolige.
Tracking People from a Mobile Platform. International Symposium on
Experimental Robotics, 2002.
- A.R. Mansouri, “Region tracking via level set PDEs without motion
computation,” PAMI, vol. 24, no. 7, pp. 947–961, 2002
- Yogesh Rathi Namrata Vaswani Allen Tannenbaum
Filtering for Geometric Active Contours with Application to Tracking
Moving and Deforming Objects, CVPR 2005
- F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce.
Segmenting, Modeling, and Matching Video Clips Containing Multiple Moving
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,
Washington, DC, June
2004, vol. 2, pp. 914-921.
- Sifakis et al., Video
Segmentation Using Fast Marching and Region Growing Algorithms, EURASIP
Journal on Applied Signal Processing 2002:4, 379–388
- 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
- J. Xiao and M. Shah, Motion layer extraction in the presence of occlusion
using graph cut, CVPR 2004
- 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.
- A. Barbu, S.C. Zhu,
By Swendsen-Wang Cuts, ICCV 2003
- S. Avidan, Support vector tracking, CVPR 2001
- Black and Jepson, Eigentracking: Robust matching and tracking of
articulated objects using a view-based representation, IJCV, 26(1), 1998
- Khan, Balch, Dellaert, A Rao-Blackwellized particle filter for
eigentracking, CVPR 2004
- Freeman and Roth, Orientation histograms for hand gesture recognition,
Workshop on AFGR, 1995
- Perez, Hue, Vermaak, Gangnet, Color-based probabilistic tracking, ECCV
- Y. Wu, Robust visual tracking by integrating multiple cues based on
co-inference learning, IJCV, 58(1), 2004
- Philomin, Duraiswami, Davis, Quasi-random sampling for condensation, ECCV
- Brown, Burschka, and Hager, Advances in Computational Stereo, PAMI 2003.
- Tao Zhang, Daniel Freedman,
Tracking Objects using Density Matching and Shape Priors, ICCV 2003
Manifold learning web page
- Belkin, Niyogi,
Laplacian eigenmaps for dimensionality reduction and data representation,
Neural Comptuation, Vol. 15, Issue 6, June 2003
Antonio Torralba Kevin P. Murphy William T. Freeman,
Sharing features: efficient boosting procedures for multiclass object
detection, CVPR 2004
Baker and Matthews,
Lucas-Kanade 20 years on: A unifying framework, IJCV 56(3):221-255,
Molton, Davison, and Reid,
Parameterisation and probability in image alignment, ACCV 2004.
A. Davison, "3D Simultaneous Localisation and Map-Building Using
Active Vision for a Robot Moving on Undulating Terrain", CVPR 2001
- Yann, LeNet-5 convolutional neural networks --
- 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.
- Morency, Rahimi, Darrell,
Adaptive View-based Appearance Model,
- M. Irani, Multi-Frame Optical Flow Estimation Using Subspace Constraints, ICCV
- Wu and Huang, A Co-inference Approach to Robust Visual
- Sigal, Sclaroff, and Athitsos, Estimation and
prediction of evolving color distributions for skin segmentation under varying
illumination, CVPR 2000
- Elgammal and Davis, Probabilistic framework for
segmenting people under occlusion, ICCV 2001
- Rui and Chen, Better proposal distributions: Object
tracking using unscented particle filter, CVPR 2001
- Toyama and Blake,
Probabilistic Tracking in a Metric Space, ICCV 2001
- H. Schneiderman, T. Kanade.
Statistical Method for 3D Object Detection Applied to Faces and Cars, CVPR
- 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.
Javed, Shafique, Shah, A hierarchical approach to robust background
subtraction using color and gradient information,
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.
sign up for the course at the beginning of the semester
attend each week
write a brief summary of each paper covered (five
sentences minimum), answering
Summaries should be turned in via email to the instructor with a subject line
What did the authors do?
What are the strengths / weaknesses / potential
ECE 904 Paper summary: Author
Author is replaced by the name of the first
author of the paper.
Non-conforming emails will be returned. Summaries are due before the
beginning of the seminar in which the paper is presented.
lead the discussion at least once during the semester. You may select a paper either from the suggested list above or
you may find one yourself. Either way, you should notify the instructor and get approval of the paper at
least one week before your presentation. Be sure not to request to
present a paper that has already been presented in previous semesters (see
(Note: A paper summary is not required for the week that you lead the