Reading list for 6.899, Learning and Inference in Vision
Spring, 2002
Learning image representations:
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Emergence of simple-cell receptive
field properties by learning a sparse code for natural
images, Olshausen BA, Field DJ (1996) Nature, 381: 607-609.
(pdf file)
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Learning the parts of objects by
non-negative matrix factorization, D. D. Lee and H. S. Seung, Nature 401, 788-791 (1999).
(pdf)
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B. W. Mel. Computational neuroscience: Think positive to
find parts (pdf), News and Views, Nature, 401, 759-760 (1999).
(pdf)
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This paper describes some details not given in the Nature paper:
Algorithms for non-negative matrix factorization, D. D. Lee and
H. S. Seung, NIPS 13, pp. 556-562 (2001).
(pdf)
Learning manifolds:
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Nonlinear Manifold Learning for Visual Speech Recognition
Christoph Bregler, Stephen M. Omohundro
Int. Conf. Computer Vision, M.I.T. 1995
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A global geometric framework for nonlinear dimensionality reduction
J. B. Tenenbaum, V. De Silva, J. C. Langford
Science 290 (5500): 22 December 2000.
(website)
(paper)
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Nonlinear dimensionality reduction by locally linear embedding.
Sam Roweis & Lawrence Saul.
Science v.290 no.5500, Dec.22, 2000. pp.2323--2326.
(website)
(paper)
Linear and bilinear models:
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S. Soatto, G. Doretto, Y.Wu
Dynamic Textures.
Intl. Conf. on Computer Vision, pages 439-446, July
2001.
(link)
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Fitzgibbon, A. W.
Stochastic rigidity: Image registration for nowhere-static scenes
Proc. International Conference on Computer Vision.
(pdf)
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Separating style and content with bilinear models.
J. B. Tenenbaum, W. T. Freeman (2000)
Neural Computation 12 (6), 1247-1283.
(pdf)
Learning low-level vision:
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R. Szeliski. Bayesian modeling of uncertainty in low-level
vision. International Journal of Computer Vision, 5(3):271-301,
December 1990
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Learning a Color Algorithm from Examples,
A. Hurlbert and T. Poggio. AI Memo 909/CBIP Paper 25, Massachusetts
Institute of Technology, Cambridge, MA, April 1987 .
Synthesizing a Color Algorithm from Examples,
A. Hurlbert and T. Poggio. Science, 239, 482-485, 1988.
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Deriving intrinsic images from image sequences
Weiss Y. to appear in ICCV 2001
(gzipped postscript)
Graphical models, belief propagation:
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian
Restoration of Images, S. Geman and D. Geman,
IEEE-PAMI, vol 6, no. 6, Nov. 1984, pp. 721-741.
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Fishler book's paper, which would now be called "Geman and Geman for Dummies".
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Parallel integration of vision modules, T. Poggio, E. B. Gamble,
J. J. Little, Science, vol. 242, pp. 337--484, October 21, 1988.
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Interpreting images by propagating Bayesian beliefs.
Weiss Y.
in: M.C. Mozer, M.I. Jordan and T. Petsche, editors, Advances in
Neural Information Processing Systems 9 908-915 (1997).
(pdf)
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Learning low-level vision, W. T. Freeman and E. C. Pasztor and
O. T. Carmichael, Intl. J. Computer Vision, vol. 40, no 1, pp. 25 --
47, 2000.
(link)
Particle filters and tracking:
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Contour tracking by stochastic propagation of conditional density
Michael Isard and Andrew Blake
Proc. European Conf. on Computer Vision, vol. 1, pp. 343--356,
Cambridge UK, (1996).
(link)
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Jepson, A.D., Fleet, D.J. and El-Maraghi, T. (2001) Robust, on-line
appearance models for vision tracking. IEEE Conference on Computer
Vision and Pattern Recognition, Kauai, Vol. I, pp. 415--422
[Runner-Up for best paper]
(pdf)
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Sidenbladh, H. and Black, M. J., Learning image statistics for
Bayesian tracking, to appear: Int. Conf. on Computer Vision,
ICCV-2001, Vancouver, BC. (postscript, 2.8MB)(pdf, 0.38MB).
(link)
Face and object recognition:
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Pattern theory : a unifying perspective, by David Mumford, in Perception as
Bayesian Inference.
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Three-dimensional object recognition from single two-dimensional
images David G. Lowe, Artificial Intelligence, 31, 3 (March 1987),
pp. 355-395.
(pdf file)
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Recognition without Correspondence using Multidimensional Receptive
Field Histograms. Bernt Schiele and James L. Crowley. In International
Journal of Computer Vision 36 (1), p 31-50, January 2000.
(gzipped postscript)
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SEEMORE:Combining color, shape, and texture histogramming in
a neuraly-inspired approach to visual object recognition. Mel, B.W., Neural
Computation, 9:777-804, 1997.
(link)
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Local feature view clustering for 3D object recognition,
David G. Lowe, IEEE Conference on Computer Vision and Pattern
Recognition, Kauai, Hawaii (December 2001).
(pdf)
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Trainable Pedestrian Detection, C. Papageorgiou and T. Poggio,
Proceedings of International Conference on Image
Processing, Kobe, Japan, October 1999.
(pdf file)
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Robust real-time object detection,
Paul Viola and Michael Jones,
2nd intl workshop on statistical and computational theories of vision,
June, 2001.
(compressed postscript)
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Bayesian Face Recognition,
Moghaddam B., Jebara T. and Pentland A., Pattern Recognition,
Vol. 33, No. 11, pps. 1771-1782, November, 2000.
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Gender classification with support vector machines,
Baback Moghaddam, Ming-Hsuan Yang, Proceedings of the 4th IEEE
International Conference on Face and Gesture Recognition, March, 2000.
(link)
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C. E Guo, S.C. Zhu, and Y. N. Wu, "Visual Learning and
Conceptualization by Integrating Descriptive and
Generative Models" Int'l J. of Computer Vision, (under review)
(gzipped postscript)
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Object recognition as machine translation--I: Learning a lexicon for a
fixed image vocabulary, Pinar Duygulu, Kobus Barnard, Nando
deFreitas, and David Forsyth, (under review).
Learning models of object appearance:
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Transformed hidden Markov models: Estimating mixture models of images
and inferring spatial transformations in video sequences.
N. Jojic, N. Petrovic, B. J. Frey and T. S. Huang.
In Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition 2000, IEEE Computer Society Press, Los Alamatos, CA.
(link)
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Markus Weber, Max Welling & Pietro Perona (2000)
Unsupervised Learning of Models for Recognition
Proc. 6th Europ. Conf. Comp. Vis., ECCV2000, Dublin
(gzipped postscript)
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Constrained Active Appearance Models, T.F.Cootes and C.J.Taylor,
Proc. ICCV 2001. Vol.I. pp748-754
(gzipped postscript)
How to write a conference paper:
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How to Get Your SIGGRAPH Paper Rejected, Jim Kajiya, SIGGRAPH 1993
Papers Chair,
(link)
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