Mercurial > cortex

     1 When I write my thesis, I want it to have links to every 

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     3 

     4 

     5 * Object Recognition from Local Scale-Invariant Features, David G. Lowe

     6   

     7   This is the famous SIFT paper that is mentioned everywhere.

     8 

     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.

    19 

    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.

    24   

    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    26   cells and object recognition,” Unpublished Manuscript, preprint at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

    28   

    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.

    33   

    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.

    37 

    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.

    42 

    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.

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    51 

    52 * Alignment by Maximization of Mutual Information, Paul A. Viola

    53 

    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. 

    57   

    58   - Pages 9-19 is a very adequate intro to the algorithm.

    59   

    60   - Has a useful section on entropy and probability at the beginning

    61     which is worth reading, especially the part about entropy.

    62 

    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?

    67 

    68   - Expectation Maximation (Mixture of Gaussians cool stuff)

    69     (Dempster 1977)

    70 

    71   - Good introduction to Parzen Window Density Estimation. Parzen

    72     density functions trade construction time for evaulation

    73     time.(Pg. 41)

    74 

    75   Occlusion? Seems a bit holistic.

    76 

    77 

    78 ** References

    79  - "excellent" book on entropy (Cover & Thomas, 1991)

    80
author	Robert McIntyre <rlm@mit.edu>
date	Tue, 12 Mar 2013 03:54:30 +0000
parents	2d8a8422ff59
children	057d47fc4789