Research on Visual Pattern Recognition with Hierarchical Neural Networks

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Aiming to develop new design principles for visual pattern recognition systems of the next generation, we have performed various researches in parallel and have improved the necognitron and the selective attention model, which were proposed previously by the author.1. Neocognitron We have succeeded in increasing robustness of the neocognitron in pattern recognition by the introduction of new network architectures and the development of improved learning methods. Performance of the improved neocognitrons has been tested for handwritten alphanumeric character recognition.(1) Thickness invariant line extraction has been realized by a new line-extraction method that uses edge information.(2) The introduction of bend-extracting cells has greatly improved the learning and recognizing ability.(3) Non-uniform blurring within a receptive field has been realized by dualization of C-cell layrs of the neocognitron, and the ability to recognize deformed patterns has been increase.(4) The shapes of receptive fields can be adaptively adjusted by creating non-uniform sensitivity within a receptive field. The ability to discriminate similar patterns can also be increased.(5) A new method of learning by error correction has been developed. The same high robustness in pattern recognition can be obtained with much smaller effort than the conventional supervised learning.(6) Large ability of generalization can be obtained by unsupervised learning with winner-take-all process, if different threshold values are used for feature-extraction in the learning and the recognition phases.2. Selective Attention A cursive English word recognition system has been developed. The system consists of the selective attention model, to which search controller and automatic attention switching mechanisms have been added. The principles of the selective attention model has been shown to be useful also for handwritten Kanji recognition and face recognition.