rlm@371: When I write my thesis, I want it to have links to every 
rlm@371: 
rlm@371: 
rlm@371: 
rlm@369: * Object Recognition from Local Scale-Invariant Features, David G. Lowe
rlm@369:   
rlm@369:   This is the famous SIFT paper that is mentioned everywhere.
rlm@369: 
rlm@369:   This is a way to find objects in images given an image of that
rlm@369:   object. It is moderately risistant to variations in the sample image
rlm@369:   and the target image. Basically, this is a fancy way of picking out
rlm@369:   a test pattern embedded in a larger pattern. It would fail to learn
rlm@369:   anything resembling object categories, for instance. Usefull concept
rlm@369:   is the idea of storing the local scale and rotation of each feature
rlm@369:   as it is extracted from the image, then checking to make sure that
rlm@369:   proposed matches all more-or-less agree on shift, rotation, scale,
rlm@369:   etc.  Another good idea is to use points instead of edges, since
rlm@369:   they seem more robust.
rlm@369: 
rlm@369: ** References:
rlm@369:  - Basri, Ronen, and David. W. Jacobs, “Recognition using region
rlm@369:   correspondences,” International Journal of Computer Vision, 25, 2
rlm@369:   (1996), pp. 141–162.
rlm@369:   
rlm@369:  - Edelman, Shimon, Nathan Intrator, and Tomaso Poggio, “Complex
rlm@369:   cells and object recognition,” Unpublished Manuscript, preprint at
rlm@369:   http://www.ai.mit.edu/edelman/mirror/nips97.ps.Z
rlm@369:   
rlm@369:  - Lindeberg, Tony, “Detecting salient blob-like image structures
rlm@369:   and their scales with a scale-space primal sketch: a method for
rlm@369:   focus-of-attention,” International Journal of Computer Vision, 11, 3
rlm@369:   (1993), pp. 283–318.
rlm@369:   
rlm@369:  - Murase, Hiroshi, and Shree K. Nayar, “Visual learning and
rlm@369:   recognition of 3-D objects from appearance,” International Journal
rlm@369:   of Computer Vision, 14, 1 (1995), pp. 5–24.
rlm@369: 
rlm@369:  - Ohba, Kohtaro, and Katsushi Ikeuchi, “Detectability, uniqueness,
rlm@369:   and reliability of eigen windows for stable verification of
rlm@369:   partially occluded objects,” IEEE Trans. on Pattern Analysis and
rlm@369:   Machine Intelligence, 19, 9 (1997), pp. 1043–48.
rlm@369: 
rlm@369:  - Zhang, Z., R. Deriche, O. Faugeras, Q.T. Luong, “A robust
rlm@369:   technique for matching two uncalibrated images through the recovery
rlm@369:   of the unknown epipolar geometry,” Artificial In- telligence, 78,
rlm@369:   (1995), pp. 87-119.
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rlm@369: 
rlm@371: * Alignment by Maximization of Mutual Information, Paul A. Viola
rlm@371: 
rlm@371:   PhD Thesis recommended by Winston. Describes a system that is able
rlm@371:   to align a 3D computer model of an object with an image of that
rlm@371:   object. 
rlm@371:   
rlm@371:   - Pages 9-19 is a very adequate intro to the algorithm.
rlm@371:   
rlm@371:   - Has a useful section on entropy and probability at the beginning
rlm@371:     which is worth reading, especially the part about entropy.
rlm@371: 
rlm@371:   - Differential entropy seems a bit odd -- you would think that it
rlm@371:     should be the same as normal entropy for a discrete distrubition
rlm@371:     embedded in continuous space. How do you measure the entropy of a
rlm@371:     half continuous, half discrete random variable?
rlm@371: 
rlm@371:   - Expectation Maximation (Mixture of Gaussians cool stuff)
rlm@371:     (Dempster 1977)
rlm@371: 
rlm@371:   - Good introduction to Parzen Window Density Estimation. Parzen
rlm@371:     density functions trade construction time for evaulation
rlm@371:     time.(Pg. 41)
rlm@371: 
rlm@371:   Occlusion? Seems a bit holistic.
rlm@371: 
rlm@371: 
rlm@371: ** References
rlm@371:  - "excellent" book on entropy (Cover & Thomas, 1991)
rlm@371: