Pattern Classification | |
| מחבר: Author: Автор: مؤلف: | Richard O. Duda, Peter E. Hart, and David G. Stork |
| הוצאה לאור: Published by: Издательство: دار نشر: | Published by: John Wiley |
| הערות: Notes: Примечания: ملاحظات: | Second Edition, 2001 |
An introduction to support vector machines (and other kernel-based learning methods) / | |
| מחבר: Author: Автор: مؤلف: | Nello Cristianini and John Shawe-Taylor |
| הוצאה לאור: Published by: Издательство: دار نشر: | Cambridge University Press, Cambridge UK, 2000. |
High level Vision | |
| מחבר: Author: Автор: مؤلف: | Shimon Ullman |
| הוצאה לאור: Published by: Издательство: دار نشر: | The MIT Press, 1996 |
Geometric Invariance in Computer Vision | |
| מחבר: Author: Автор: مؤلف: | J.L. Mundy and A. Zisserman (eds.) |
| הוצאה לאור: Published by: Издательство: دار نشر: | The MIT Press, Cambridge, Massachusetts 1992. |
Papers | |
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Basic PCA Theory Notes: Turk, M. and Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71--86. 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. Peter N. Belhumeur, Joao Hespanha and David J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, ECCV,pp.45-58,1996, url = citeseer.nj.nec.com/belhumeur96eigenfaces.html De la Torre, F. and Black, M. J., Robust principal component analysis for computer vision, Int. Conf. on Computer Vision, ICCV-2001, Vancouver, R. Goldenberg, R. Kimmel, E. Rivlin, M. Rudzsky , Dynamism of a Dog on a Leash or Behavior Classification by Eigen-decomposition of Periodic Motions, ECCV 2002 T. Steinherz, N. Intrator and E. Rivlin. Skew Correction via Principal Component Analysis. In Proceedings International Conference on Documents Analysis and Recognition, pages xxx-yyy, 1999. Papers Basic SVM Theory Notes: Christopher J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol. 2, num. 2, pp. 121-167, 1998, url =citeseer.nj.nec.com/article/burges98tutorial.html Projects (to prepare in pairs) Articles for projects Notes: E. Rivlin, M. Rudzsky ,R Goldenberg,U. Bogomolov, S. Lepchev, A Real-Time System for Classification of Moving Objects, sent to ICPR-2002, R. Goldenberg, R. Kimmel, E. Rivlin, M. Rudzsky , Dynamism of a Dog on a Leash or Behavior Classification by Eigen-decomposition of Periodic Motions, ECCV 2002 Baback Moghaddam, Alex Pentland, Probabilistic Visual Learning for Object Representation IEEE Transactions on Pattern Analysis and Machine Intelligence vol.19, n.7 pp. 696-710, 1997. M.H. Yang, N., Ahuja, D. Kriegman, Face Recognition Using Kernel Eigenfaces, Int. Conf. on Image Processing, 2000, vol. 1, pp. 37-40 M. Betke and N. C. Makris, Recognition, Resolution, and Complexity of Objects Subject to Affine Transformations, International Journal of Computer Vision 44(1), 5-40,2001 Usage of Hidden Markov Models in different approaches: segmentation-based and segmentation-free: 1. M.Y. Chen, A. Kundu and J. Zhou. Off-line Handwritten Word Recognition Using a Hidden Markov Model Type Stochastic Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(5):481:496, 1994. 2. M. Mohamed and P. Gader. Handwritten Word Recognition Using Segmentation-Free Hidden Markov Modeling and Segmentation-Based Dynamic Programming Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(5):548:554, 1996. Papers Basic Hidden Markov Model Notes: L. R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. IEEE Proceedings 77(2):257-286, 1989. |
Papers | |
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De la Torre, F. and Black, M. J., Robust principal component analysis for computer vision, Int. Conf. on Computer Vision, ICCV-2001, Vancouver, R. Goldenberg, R. Kimmel, E. Rivlin, M. Rudzsky , Dynamism of a Dog on a Leash or Behavior Classification by Eigen-decomposition of Periodic Motions, ECCV 2002 T. Steinherz, N. Intrator and E. Rivlin. Skew Correction via Principal Component Analysis. In Proceedings International Conference on Documents Analysis and Recognition, pages xxx-yyy, 1999. |
SVM Tutorial | |
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Papers Basic SVM Theory Notes: Christopher J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol. 2, num. 2, pp. 121-167, 1998, url =citeseer.nj.nec.com/article/burges98tutorial.html |
Papers | |
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Basic Hidden Markov Model Notes: L. R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. IEEE Proceedings 77(2):257-286, 1989. |
Computer Vision -- A modern approach | |
| מחבר: Author: Автор: مؤلف: | David Forsyth and Jean Ponce |
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Very useful reference You can find useful information concerning the last themes of our course (as well as all the themes in computer vision) in a great book (available on the net) of David Forsyth and Jean Ponce, Computer Vision -- A modern approach http://www.cs.berkeley.edu/~daf/book.html. Read their chapters 1 and 20. | |
Articles for the Part Two of the course | |
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Biederman I. Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review 94:115-147 (1987). D.D. Hoffman, W.A. Richards. (1984). Parts of recognition. Cognition, 18, 65-96. (Also appears as MIT AI Memo 732, 1983; in Visual Cognition, S. Pinker (Ed), MIT Press, 1985; in From Pixels to Predicates: Recent Advances in Computational Vision, A. Pentland (Ed), Ablex Publishing Company, 1986; and in Readings in Computer Vision, M. Fischler and O. Firschein (Eds), Morgan and Kaufmann Publishers, Inc., 1987.) Q.L.Nguyen,M.D. Levine Representing 3D objects in Range Images Using Geons CVIU,v.63,No.1, January, pp.158-168,1996 http://www.cc.gatech.edu/conferences/iuw/abstracts/abstract141.html Ehud Rivlin,Sven J. Dickinson and Azriel Rosenfeld Object Recognition by Functional Parts and Recognition by Functional Parts E. Rivlin, S. Dickinson, and A. Rosenfeld, Computer Vision and Image Understanding, special issue on Function-based Object Recognition, Vol. 62, No. 2, September, 1995, pp 164--176. |
Articles cont. | |
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CVIU March 1999 and CVIU v.74,No.3,June,pp.163-173,1999 On Estimating the Uncertainty in the Location of Image Points in 3D Recognition from Match Sets of Different Sizes Ilan Shimshoni and Jean Ponce Probabilistic 3D Object Recognition, IJCV 36(1),51-70 (2000) Carlo Tomasi and Takeo Kanade. Shape and motion from image streams under orthography: a factorization method. International Journal of Computer Vision, 9(2):137-154, November 1992. Last updated on 13/6/2002, 09:27:06 Projective Geometry http://robotics.stanford.edu/~birch/projective/ An Introduction to Projective Geometry (for computer vision) - Stan Birchfield The contents of this paper include: The Projective Plane; Projective Space; Projective Geometry Applied to Computer Vision; Demonstration of Cross Ratio in P^1; and a bibliography. http://www.inrialpes.fr/movi R.Mohr and B. Triggs Projective Geometry for Image Analysis Created on 13/6/2002, 08:33:45 Geometric Hashing Haim J. Wolfson. "Model-Based Object Recognition by Geometric Hashing." Yehezkel Lamdan, Haim J. Wolfson. "Geometric Hashing: A General and Efficient Model-Based Recognition Scheme." Proceedings of the Second International Conference on Computer Vision, Tampa, FL, December 5-8, 1988. |