Neuromorphic model for information fusion

A neural architecture is presented for fusion of multisensory information at the feature level. The inputs to the network are in the form of binary edge patterns from multiple sensors. Each input is processed by a hierarchical neural structure similar to the forward path of MARA, which was previously proposed by the authors (1990). Since the neurons at higher levels are insensitive to distortion, noise, scaling, and displacement of the activities at lower levels, the activities of higher-level neurons in these networks processing different sensor information can be combined. The proposed architecture consists of a fusion path to carry the combined information to indicate the final decision. The fusion architecture is used to recognize the traces of trained patterns in low-quality images from different sensors