List of Articles for Lectures and Implementation Recognition and Tracking



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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 Be-

haviors", 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 IJCV 2004

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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