List of Articles for Lectures and Implementation
Recognition and Tracking
1. A Graphical Model for Audiovisual Object Tracking Matthew J. Beal, Nebojsa Jojic, and Hagai Attias
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO.
7, JULY 2003
2. Transformation-Invariant Clustering Using the EM
Algorithm, Brendan J. Frey, , and Nebojsa Jojic, TRANSACTIONS ON PATTERN ANALYSIS AND
MACHINE INTELLIGENCE, VOL. 25, NO. 1, JANUARY 2003
3. Multiple Frame Motion Inference Using Belief Propagation
Jiang Gao Jianbo Shi. Six Int. Conf. on Automatic Face and Gesture Recognition Seul 2004
4. Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video Michalis K. Titsias and
Christopher K. I. Williams
5. Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation
Erik B. Sudderth, Michael I. Mandel, William T. Freeman, and Alan S. Willsky
6. Attractive People: Assembling Loose-Limbed Models using Non-parametric Belief Propagation
Leonid Sigal, Michael Isard, Benjamin H. Sigelman, Michael J. Black
7. PAMPAS: Real-Valued Graphical Models for Computer Vision M. Isard
8. A Hierarchical Bayesian Network for Event Recognition of Human Actions and Interactions
Sangho Park, J.K. Aggarwal
9. A Reliable-Inference Framework for Recognition of Human Actions_James W. Davis Ambrish Tyagi
10. Unsupervised Learning of Human Motion Yang Song, Luis Goncalves, and Pietro Perona,
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO.
7, JULY 2003
11. Representation and Matching of Articulated Shapes
Jiayong Zhang, Robert Collins and Yanxi Liu
12. Finding and Tracking People from the Bottom Up
Deva Ramanan and D. A. Forsyth
13. Enriching a Motion Collection by Transplanting Limbs
Leslie Ikemoto and David A. Forsyth1 Eurographics/ACM SIGGRAPH Symposium on Computer
Animation (2004)
14. Extending Pictorial Structures for Object Recognition
M. Pawan Kumar P.H.S. Torr A. Zisserman
15.H. Dee and D. Hogg (2004), Detecting inexplicable behaviour, British Machine Vision Conference
2004 (BMVC-04), Kingston. pdf
Recognition by Function
16.Duric, Z., Fayman, J., and Rivlin, E., Function from Motion", IEEE Trans. On Pattern Analysis and
Machine Intelligence, pp. 579-591, June, 1996.
17.Duric, Z., Rivlin, E., Rosenfeld, A., Understanding Object Motion", Image and Vision
Computing, Vol. 16, pp. 785-797, 1998.
18.Dar, T., Joskowicz, L., Rivlin, E., \Understanding Mechanisms: From Images to Behaviors", Journal of Arti_cial Intelligence, Vol. 112, No. 1-2, pp. 147-179, 1999.
Alignment
19.Ronen Basri and David Jacobs, Recognition Using Region Correspondences, International Journal of
Computer Vision, 25(2): 141-162, 1997.
20.Ronen Basri and Yael Moses, When Is It Possible to Identify 3D Objects from Single Images Using
Class Constraints?, International Journal of Computer Vision, 33(2): 40-61, 1999.
21.Ronen Basri, Recognition by Prototypes, International Journal of Computer Vision, 19(2): 147-168,
1996.
22.Vetter, T., Poggio, T.,
Linear Object Classes and Image Synthesis from a Single Example Image,
PAMI(19), No. 7, July 1997, pp. 733-742.
Vetter, T., Poggio, T.,
Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and
Symmetries,MIT AI Memo-1347, 1992. Generation of realistic images from different viewpoints given
only one input image.
23.Grimson, W.E.L.,
The Combinatorics of Object Recognition in Cluttered Environments Using Constrained Search,
AI(44), No. 1-2, July 1990, pp. 121-166.
Earlier: ICCV88(218-227).
IEEE Abstract. IEEE Top Reference. BibRef
And: MIT AI Memo-1019, February 1988
24.Sarachik, K.B.[Karen B.],
The Effect of Gaussian Error in Object Recognition,
PAMI(19), No. 4, April 1997, pp. 289-301.
Recognition by parts
25.Wu, K.N., Levine, M.D.,
3-D Shape Approximation Using Parametric Geons,
IVC(15), No. 2, February 1997, pp. 143-158.
WWW Version. 9702 BibRef
26.Zerroug, M., Nevatia, R.,
Part-Based 3D Descriptions of Complex Objects from a Single Image,
PAMI(21), No. 9, September 1999, pp. 835-848.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9909
27.Dickinson, S.J., Pentland, A.P., and Rosenfeld, A.,
From Volumes to Views: An Approach to 3-D Object Recognition,
CVGIP(55), No. 2, March 1992, pp. 130-154.
WWW Version. BibRef 9203
28.Grimson, W.E.L., and Lozano-Perez, T.,
Localizing Overlapping Parts by Searching the Interpretation Tree,
PAMI(9), No. 4, July 1987, pp. 469-482. BibRef 8707
And:
Recognition and Localization of Overlapping Parts from Sparse Data,
3DMV87(451-510). BibRef
And: MIT AI Memo-841, June 1985. BibRef
29.Pictorial Structures for Object Recognition
Pedro F. Felzenszwalb‫ת‬Daniel P. Huttenlocher IJCV2004
30. Santos, Paulo; Magee, Derek; Cohn, Anthony; Hogg, David. Combining multiple answers for learning
mathematical structures from visual observation in: Lopez de Mantaras, R & Saitta, L (editors) ECAI 2004
Proceedings of the 16th European Conference on Artificial Intelligence, pp. 544-548 IOS Press. 2004.
31 Galata A., Cohn A., Magee D. and Hogg D., Modeling Interaction Using Learnt Qualitative Spatial
Relations and Variable Length Markov Models, Proc. 15th European Conference on Artificial
Intelligence, Lyon, 2002. Available Here.
Face Detection and Tracking
32.Hierarchical classi_cation andfeature reduction for fast face detection with support vector machines
Bernd Heisele, Thomas Serre , Sam Prentice , Tomaso Poggio, Pattern Recognition 36 (2003) 2007 – 2017
33.Face Detection: A Survey Erik Hjelm andBoon Kee Low, Computer Vision and Image Understanding
83, 236–274 (2001)
34.The CSU Face Identification Evaluation System Its purpose, features, and structure
J. Ross Beveridge, David Bolme, Bruce A. Draper, Marcio Teixeira,
Machine Vision and Applications (2005) 16: 128–138
35.Robust real-time face tracker for cluttered environments
Keith Anderson and Peter W. McOwan*,Computer Vision and Image Understanding 95 (2004) 184–200
36. Using Component Features for Face Recognition.Yuri Ivanov Bernd Heisele Thomas Serre
37. P.Viola, M.Jones,Robust Real-Time Face Detection International Journal of Computer Vision
Volume 57 , Issue 2 (May 2004) Pages: 137 - 154
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