Abstract
A new model which can recognize rotated, distorted, scaled, shifted and noised patterns is proposed. The model is constructed based on psychological experiments in a mental rotation. The model has two types of processes: (i) one is a bottom-up process in which pattern recognition is realized by means of a rotation-invariant neocognitron and a standard neocognitron and (ii) the other is a top-down process in which a mental rotation is executed by means of a model of associative recall in visual pattern recognition. In computer simulations, it is shown that the model can recognize rotated patterns without training those patterns.
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© 1999 Springer-Verlag Berlin Heidelberg
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Satoh, S., Aso, H., Miyake, S., Kuroiwa, J. (1999). Pattern recognition system with top-down process of mental rotation. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098240
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DOI: https://doi.org/10.1007/BFb0098240
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