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

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.