Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Analysis of Thinning Algorithms Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shift invariance and the neocognitron
Neural Networks
Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
One-Pass Parallel Thinning: Analysis, Properties, and Quantitative Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Network model for invariant object recognition
Pattern Recognition Letters
A hierarchical multiple-view approach to three-dimensional object recognition
IEEE Transactions on Neural Networks
Handwritten alphanumeric character recognition by the neocognitron
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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A new technique based on self-organization is proposed for classifying patterns (which include characters, and two- and three-dimensional objects). A neuronal network, created to be a physical replica of each exemplar, is mapped onto the given test pattern by self-organization, during which the network undergoes deformation in an attempt to match the given test pattern. The extent of deformation is inversely proportional to the correctness of the match: smaller the deformation, better is the match. A deformation measure is proposed, leading to the classification of the test pattern. Also presented are some algorithmic improvements (including the choice of other deformation measures) to speed up computation. Examples illustrate the versatility of the technique.