3D target recognition using cooperative feature map binding under Markov Chain Monte Carlo

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A robust and effective feature map integration method is presented for infrared (IR) target recognition. Noise in an IR image makes a target recognition system unstable in pose estimation and shape matching. A cooperative feature map binding under computational Gestalt theory shows robust shape matching properties in noisy conditions. The pose of a 3D target is estimated using a Markov Chain Monte Carlo (MCMC) method, a statistical global optimization tool where noise-robust shape matching is used. In addition, bottom-up information accelerates the recognition of 3D targets by providing initial values to the MCMC scheme. Experimental results show that cooperative feature map binding by analyzing spatial relationships has a crucial role in robust shape matching, which is statistically optimized using the MCMC framework. (c) 2005 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2006-05
Language
English
Article Type
Article
Keywords

3-D OBJECTS; REPRESENTATION

Citation

PATTERN RECOGNITION LETTERS, v.27, pp.811 - 821

ISSN
0167-8655
URI
http://hdl.handle.net/10203/20579
Appears in Collection
EE-Journal Papers(저널논문)
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