Object recognition and tracking with maximum likelihood bidirectional associative memory networks

Abstract

A maximum-likelihood-criterion based bidirectional associative memory network (hereinafter, the MLBAM network) is presented, which can be employed to evaluate the similarity between a template and a matching region. Furthermore, the analysis on the stability and the convergence of learning rule of the network is conducted. The results show that the MLBAM network is capable of associating two templates (big and small) and thus greatly reducing the computational load by using coarse-to-fine hierarchical strategy. Finally, an experiment on the target tracking of MLBAM network is conducted using a group of robots operating on a football field, demonstrating the high efficiency of the method.