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1 When I write my thesis, I want it to have links to every
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2
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3
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4
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5 * Object Recognition from Local Scale-Invariant Features, David G. Lowe
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6
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7 This is the famous SIFT paper that is mentioned everywhere.
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8
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9 This is a way to find objects in images given an image of that
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10 object. It is moderately risistant to variations in the sample image
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11 and the target image. Basically, this is a fancy way of picking out
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12 a test pattern embedded in a larger pattern. It would fail to learn
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13 anything resembling object categories, for instance. Usefull concept
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14 is the idea of storing the local scale and rotation of each feature
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15 as it is extracted from the image, then checking to make sure that
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16 proposed matches all more-or-less agree on shift, rotation, scale,
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17 etc. Another good idea is to use points instead of edges, since
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18 they seem more robust.
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19
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20 ** References:
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21 - Basri, Ronen, and David. W. Jacobs, “Recognition using region
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22 correspondences,” International Journal of Computer Vision, 25, 2
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23 (1996), pp. 141–162.
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24
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25 - Edelman, Shimon, Nathan Intrator, and Tomaso Poggio, “Complex
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26 cells and object recognition,” Unpublished Manuscript, preprint at
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27 http://www.ai.mit.edu/edelman/mirror/nips97.ps.Z
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28
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29 - Lindeberg, Tony, “Detecting salient blob-like image structures
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30 and their scales with a scale-space primal sketch: a method for
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31 focus-of-attention,” International Journal of Computer Vision, 11, 3
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32 (1993), pp. 283–318.
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33
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34 - Murase, Hiroshi, and Shree K. Nayar, “Visual learning and
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35 recognition of 3-D objects from appearance,” International Journal
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36 of Computer Vision, 14, 1 (1995), pp. 5–24.
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37
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38 - Ohba, Kohtaro, and Katsushi Ikeuchi, “Detectability, uniqueness,
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39 and reliability of eigen windows for stable verification of
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40 partially occluded objects,” IEEE Trans. on Pattern Analysis and
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41 Machine Intelligence, 19, 9 (1997), pp. 1043–48.
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42
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43 - Zhang, Z., R. Deriche, O. Faugeras, Q.T. Luong, “A robust
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44 technique for matching two uncalibrated images through the recovery
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45 of the unknown epipolar geometry,” Artificial In- telligence, 78,
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46 (1995), pp. 87-119.
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47
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48
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49
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50
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51
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52 * Alignment by Maximization of Mutual Information, Paul A. Viola
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53
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54 PhD Thesis recommended by Winston. Describes a system that is able
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55 to align a 3D computer model of an object with an image of that
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56 object.
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57
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58 - Pages 9-19 is a very adequate intro to the algorithm.
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59
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60 - Has a useful section on entropy and probability at the beginning
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61 which is worth reading, especially the part about entropy.
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62
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63 - Differential entropy seems a bit odd -- you would think that it
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64 should be the same as normal entropy for a discrete distrubition
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65 embedded in continuous space. How do you measure the entropy of a
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66 half continuous, half discrete random variable?
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67
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68 - Expectation Maximation (Mixture of Gaussians cool stuff)
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69 (Dempster 1977)
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70
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71 - Good introduction to Parzen Window Density Estimation. Parzen
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72 density functions trade construction time for evaulation
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73 time.(Pg. 41)
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74
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75 Occlusion? Seems a bit holistic.
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76
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77
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78 ** References
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79 - "excellent" book on entropy (Cover & Thomas, 1991)
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80 |
