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.
Helmut Grabner, Jiri Matas, Luc Van Gool, Philippe Cattin.
Tracking the Invisible: Learning Where the Object Might Be, CVPR 2010.
Charles Bibby and Ian Reid,
Real-time Tracking of Multiple Occluding Objects using Level Sets, CVPR
2010 (CVPR 2010
Antonio Torralba, "How many pixels make an image?", Visual Neuroscience, 2010||
S. Stalder, H. Grabner, and L. Van Gool,
Beyond Semi-Supervised Tracking: Tracking Should Be as Simple as Detection, but not Simpler
than Recognition, In Proceedings ICCV’09 WS on On-line Learning for
Computer Vision, 2009.
Bangpeng Yao and Li Fei-Fei,
Mutual Context of Object and Human Pose in Human-Object Interaction
Activities, IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
|Hui Kong, Jean-Yves Audibert, and Jean Ponce.
General Road Detection
from A Single Image. IEEE Transactions on Image Processing, Aug.
Salzmann and Urtasun
Combining Discriminative and Generative Methods for 3D Deformable Surface
and Articulated Pose Reconstruction CVPR 2010.
Y. Li and S. Birchfield.
Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot,
B. Willimon, S. Birchfield, and I. Walker.
Rigid and Non-Rigid Classification Using Interactive Perception, IROS
Bryan Willimon /
[out of town]
Delage, E. Honglak Lee Ng, A.Y. A
Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image, CVPR 2006
Steve Gu and Ying Zheng and Carlo Tomasi.
Nets and Beta-Stable Features for Image Matching, ECCV 2010.
Georg Klein and David Murray,
Parallel Tracking and Mapping for Small AR Workspaces, ISMAR 2007
Jianguo Li, Eric Li, Yurong Chen, Li Xu, Yimin Zhang,
Bundled Depth-Map Merging for Multi-View Stereo, CVPR 2010.
Yiyan Wang, Yuexian Zou, Hang Shi, He Zhao. Video
Image Vehicle Detection System for Signaled Traffic Intersection. HIS
Papers covered in previous semesters
Huang, Guestrin, and Guibas,
Fourier Theoretic Probabilistic Inference over Permutations, JMLR 2009
Dense Point Trajectories by GPU-accelerated Large
Displacement Optical Flow,
Matthias Grundmann, Vivek Kwatra, Mei Han, and Irfan
Hierarchical Graph-Based Video Segmentation”, CVPR 2010.
Ying Nian Wu, Zhangzhang Si, Chuck Fleming, and Song-Chun Zhu,
Deformable Template As Active Basis, ICCV 2007
Wu and Nevatia. "Detection and Tracking of Multiple,
Partially Occluded Humans by Bayesian Combination of Edgelet based Part
Detectors." IJCV 2007.
- Imran N. Junejo, Emilie Dexter, Ivan Laptev and Patrick Perez, Cross-View
Action Recognition from Temporal
Self-Similarities, ECCV 2008
SIFT, SURF & seasons: Appearance-based long-term localization in outdoor
environments Robotics and Autonomous Systems
Volume 58, Issue 2, 2010.
- 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