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beginning extensive literature review.
author Robert McIntyre <rlm@mit.edu>
date Sun, 10 Mar 2013 18:17:53 +0000
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children 9c37a55e1cd2
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1 * Object Recognition from Local Scale-Invariant Features, David G. Lowe
3 This is the famous SIFT paper that is mentioned everywhere.
5 This is a way to find objects in images given an image of that
6 object. It is moderately risistant to variations in the sample image
7 and the target image. Basically, this is a fancy way of picking out
8 a test pattern embedded in a larger pattern. It would fail to learn
9 anything resembling object categories, for instance. Usefull concept
10 is the idea of storing the local scale and rotation of each feature
11 as it is extracted from the image, then checking to make sure that
12 proposed matches all more-or-less agree on shift, rotation, scale,
13 etc. Another good idea is to use points instead of edges, since
14 they seem more robust.
16 ** References:
17 - Basri, Ronen, and David. W. Jacobs, “Recognition using region
18 correspondences,” International Journal of Computer Vision, 25, 2
19 (1996), pp. 141–162.
21 - Edelman, Shimon, Nathan Intrator, and Tomaso Poggio, “Complex
22 cells and object recognition,” Unpublished Manuscript, preprint at
23 http://www.ai.mit.edu/edelman/mirror/nips97.ps.Z
25 - Lindeberg, Tony, “Detecting salient blob-like image structures
26 and their scales with a scale-space primal sketch: a method for
27 focus-of-attention,” International Journal of Computer Vision, 11, 3
28 (1993), pp. 283–318.
30 - Murase, Hiroshi, and Shree K. Nayar, “Visual learning and
31 recognition of 3-D objects from appearance,” International Journal
32 of Computer Vision, 14, 1 (1995), pp. 5–24.
34 - Ohba, Kohtaro, and Katsushi Ikeuchi, “Detectability, uniqueness,
35 and reliability of eigen windows for stable verification of
36 partially occluded objects,” IEEE Trans. on Pattern Analysis and
37 Machine Intelligence, 19, 9 (1997), pp. 1043–48.
39 - Zhang, Z., R. Deriche, O. Faugeras, Q.T. Luong, “A robust
40 technique for matching two uncalibrated images through the recovery
41 of the unknown epipolar geometry,” Artificial In- telligence, 78,
42 (1995), pp. 87-119.