A Neural Network Model for Pattern Recognition Based on Hypothesis and Verification with Moving Region of Attention

Authors:
Masao Shimomura;Shunji Satoh;Shogo Miyake;Hirotomo Aso
Affiliations:
-;-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

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Abstract

We present a neural network model for pattern recognition which works successfully even if only a part of a pattern is presented on the retina. During the recognition process, (i) local features are extracted, (ii) a hypothesis for a partial pattern is generated using shift-invariant features, (iii) the hypothesis is verified by collating with the real positions of features. The verification process gradually corrects positional displacement of the presented partial pattern while the processes (i)-(iii) are executed. Computer simulations show that the model is tolerant for vast amounts of shift, deformation and noise.