An artificial neural network that self-organizes to recognize various images presented as a training set is described. One application of the network uses multiple functionally disjoint stages to provide pattern recognition that is invariant to translations of the object in the image plane. The general form of the network uses three stages that perform the functionally disjoint tasks of preprocessing, invariance, and recognition. The preprocessing stage is a single layer of processing elements that performs dynamic thresholding and intensity scaling. The invariance stage is a multilayered connectionist implementation of a modified Walsh-Hadamard transform used for generating an invariant representation of the image. The recognition stage is a multilayered self-organizing neural network that learns to recognize the representation of the input image generated by the invariance stage. The network can successfully self-organize to recognize objects without regard to the location of the object in the image field and has some resistance to noise and distortions.>

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

This paper presents a pattern recognition system that self-organizes to recognize objects by shape as part of an Integrated Visual Network (IVN) for autonomous flight control. The system uses a multistaged hierarchical neural network that exhibits insensitivity to the location of the object in the visual field. The network's three layers perform the functionally disjoint tasks of preprocessing (dynamic thresholding), invariance (position normalization), and recognition (identification of the shape). The Preprocessing stage uses a single layer of elements to dynamically threshold the grey level input image into a binary image. 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 Fukushima's Neocognitron that identifies the position normalized representation by shape. The inclusion of the Preprocessing and Invariance stages allows reduction of the massively replicated processing structures used for translation invariance in the Neocognitron. 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.