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Object recognition kunal
1. 28/12/2014 Object Recognition
http://www.cs.rochester.edu/~nelson/research/recognition/recognition.html 1/8
Object Recognition Research
Randal C. Nelson
Department of Computer Science
University of Rochester
Appearancebased object recognition methods have
recently demonstrated good performance on a
variety of problems. However, many of these
methods either require good wholeobject
segmentation, which severely limits their
performance in the presence of clutter, occlusion, or
background changes; or utilize simple conjunctions of lowlevel
features, which causes crosstalk problems as the number of objects is
increased. We are investigating an appearancebased object
recognition system using a keyed, multilevel context representation,
that ameliorates many of these problems, and can be used with
complex, curved shapes. Pictures on this page are from a training
database we have used in system tests.
Specifically, we utilize distinctive intermediatelevel features in this
case automatically extracted 2D boundary fragments, as keys, which
are then verified within a local context, and assembled within a loose
global context to evoke an overall percept. The system demonstrates
extraordinarily good recognition of a variety of 3D shapes, ranging
from sports cars and fighter planes to snakes and lizards with full
orthographic invariance. We have performed a number of largescale
experiments, involving over 2000 separate test images, that evaluate
performance with increasing number of items in the database, in the