Modified neocognitron with position normalizing preprocessor for translation invariant shape recognition

A pattern-recognition system that self-organizes to recognize objects by shape as part of an integrated visual network (IVN) for autonomous flight control is presented. The system uses a multistaged hierarchical neural network that exhibits insensitivity to the location of the object in the visual field. The network´s two layers perform the functionally disjoint tasks of invariance (position normalization) and recognition (identification of the shape). The invariance stage is a multilayered neural network implementation of a modified Walsh-Hadamard transform that generates a representation of the object that is invariant with respect to the object´s position. The recognition stage is a modified version of the Fukushima neocognitron that identifies the position-normalized representation by shape. The inclusion of the invariance stage allows reduction of the massively replicated processing structures used for translation invariance in the neocognition. This system offers roughly the same translation invariance capabilities as the neocognitron, with a dramatic reduction in the number of elements and the network´s interconnection complexity