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[International Journal] 3D Target Recognition using Cooperative Feature Map Binding under Markov Chain Monte Carlo
Pattern Recognition Letters , May 2006
  PRL2006_cooperative_shkim.pdf PRL2006_cooperative_shkim.pdf (895.9K) [107]
27 (7): 811-821

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.


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