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author Robert McIntyre <rlm@mit.edu>
date Tue, 12 Mar 2013 04:23:55 +0000
parents 9c37a55e1cd2
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1 When I write my thesis, I want it to have links to every
5 * Object Recognition from Local Scale-Invariant Features, David G. Lowe
7 This is the famous SIFT paper that is mentioned everywhere.
9 This is a way to find objects in images given an image of that
10 object. It is moderately risistant to variations in the sample image
11 and the target image. Basically, this is a fancy way of picking out
12 a test pattern embedded in a larger pattern. It would fail to learn
13 anything resembling object categories, for instance. Usefull concept
14 is the idea of storing the local scale and rotation of each feature
15 as it is extracted from the image, then checking to make sure that
16 proposed matches all more-or-less agree on shift, rotation, scale,
17 etc. Another good idea is to use points instead of edges, since
18 they seem more robust.
20 ** References:
21 - Basri, Ronen, and David. W. Jacobs, “Recognition using region
22 correspondences,” International Journal of Computer Vision, 25, 2
23 (1996), pp. 141–162.
25 - Edelman, Shimon, Nathan Intrator, and Tomaso Poggio, “Complex
26 cells and object recognition,” Unpublished Manuscript, preprint at
27 http://www.ai.mit.edu/edelman/mirror/nips97.ps.Z
29 - Lindeberg, Tony, “Detecting salient blob-like image structures
30 and their scales with a scale-space primal sketch: a method for
31 focus-of-attention,” International Journal of Computer Vision, 11, 3
32 (1993), pp. 283–318.
34 - Murase, Hiroshi, and Shree K. Nayar, “Visual learning and
35 recognition of 3-D objects from appearance,” International Journal
36 of Computer Vision, 14, 1 (1995), pp. 5–24.
38 - Ohba, Kohtaro, and Katsushi Ikeuchi, “Detectability, uniqueness,
39 and reliability of eigen windows for stable verification of
40 partially occluded objects,” IEEE Trans. on Pattern Analysis and
41 Machine Intelligence, 19, 9 (1997), pp. 1043–48.
43 - Zhang, Z., R. Deriche, O. Faugeras, Q.T. Luong, “A robust
44 technique for matching two uncalibrated images through the recovery
45 of the unknown epipolar geometry,” Artificial In- telligence, 78,
46 (1995), pp. 87-119.
52 * Alignment by Maximization of Mutual Information, Paul A. Viola
54 PhD Thesis recommended by Winston. Describes a system that is able
55 to align a 3D computer model of an object with an image of that
56 object.
58 - Pages 9-19 is a very adequate intro to the algorithm.
60 - Has a useful section on entropy and probability at the beginning
61 which is worth reading, especially the part about entropy.
63 - Differential entropy seems a bit odd -- you would think that it
64 should be the same as normal entropy for a discrete distrubition
65 embedded in continuous space. How do you measure the entropy of a
66 half continuous, half discrete random variable?
68 - Expectation Maximation (Mixture of Gaussians cool stuff)
69 (Dempster 1977)
71 - Good introduction to Parzen Window Density Estimation. Parzen
72 density functions trade construction time for evaulation
73 time.(Pg. 41)
75 Occlusion? Seems a bit holistic.
78 ** References
79 - "excellent" book on entropy (Cover & Thomas, 1991)