The topics studied in this course will include: - Image statistics, image representations, and texture models
- Color Vision
- Graphical models, Bayesian methods
- Markov Random Fields, applications to low-level vision
- Approximate inference methods
- Statistical classifiers
- Clustering & Segmentation
- Object recognition
- Tracking and Density Propagation
- Visual Surveillance and Activity Monitoring
- E. H. Adelson, E. P. Simoncelli, and W. T. Freeman,
**Pyramids and Multiscale Representations**. In*Representations of Vision*, pp. 3-16, 1991. - S. Lyu and H. Farid ,
**How Realistic is Photorealistic?**. In*IEEE Transactions on Signal Processing*, 53(2):845-850, 2005. - D. G. Lowe,
**Distinctive Image Features from Scale-Invariant Keypoints**. In*International Journal of Computer Vision*, 2004. - Allan Jepson ,
**Local Feature Tutorial**. - K. Mikolajczyk and C. Schmid,
**A performance evaluation of local descriptors**. In*IEEE Conference on Computer Vision and Pattern Recognition*, pp. 257-263, 2003. - S. Belongie, J. Malik and J. Puzicha,
**Shape Matching and Object Recognition Using Shape Contexts**. In*IEEE Transactions on Pattern Analysis and Machine Intelligence*, Volume 24 , Issue 4 (April 2002), pp. 509 - 522. - M.I. Jordan and Y. Weiss,
**Probabilistic inference in graphical models**. In*Arbib, M. (ed): Handbook of Neural Networks and Brain Theory. 2nd edition. MIT Press*, 2002. - Kevin Murphy,
**A Brief Introduction to Graphical Models and Bayesian Networks**1998. -
**Matlab MRF example codes**. - Baback Moghaddam and Alex Pentland,
**Probabilistic Visual Learning for Object Representation**. In*Early Visual Learning, Oxford University Press*, 1996. - B. Moghaddam and M-H. Yang,
**Gender Classification with Support Vector Machines**. In*Proceedings of the 4th IEEE Int'll Conf. on Face and Gesture Recognition*, 2000. - B. Moghaddam, T. Jebara and A. Pentland,
**Bayesian Face Recognition**. In*Pattern Recognitionn, Vol. 33, No. 11, pps. 1771-1782, November*, 2000. - Bernhard Scholkopf,
**Statistical Learning and Kernel Methods**. In*Microsoft Research Technical Report*, 2000. - M. Weber, M. Welling and P. Perona,
**Unsupervised Learning of Models for Recognition**. In*ECCV*, 2000. - D. Comaniciu and P.Meer,
**Mean Shift: A Robust Approach Toward Feature Space Analysis.***EEE Transactions on Pattern Analysis and Machine Intelligence*, 2002. - W. Freeman,
**How to write a conference paper.**.2002. - D. Forsyth and J. Ponce,
**Computer Vision: A Modern Approach, Extra Chapter**. Prentice Hall, 2002.
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