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rlm@369:   cells and object recognition,” Unpublished Manuscript, preprint at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.
rlm@369: 
rlm@369:     
rlm@369:      
rlm@369: 
rlm@369: 
rlm@369:     
rlm@369: 
rlm@369: