Neural network model restoring partly occluded patterns

Authors:
Kunihiko Fukushima
Affiliations:
Tokyo University of Technology, Hachioji, Tokyo 192-0982, Japan. E-mail: fukushima@media.teu.ac.jp
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems - Advanced Intelligent Techniques in Engineering Applications
Year:
2004

Citing 2
Cited 1

Intermediate-level visual representations and the construction of surface perception

Journal of Cognitive Neuroscience
Restoring partly occluded patterns: a neural network model with backward paths

ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing

Object recognition using a bio-inspired neuron model with bottom-up and top-down pathways

Neurocomputing

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Abstract

Even an identical image is perceived differently by human beings depending on the shape of occluding objects. This paper proposes a neural network model that has an ability to recognize and restore partly occluded patterns in a similar way as our perception. It is a multi-layered hierarchical neural network, in which visual information is processed by interaction of bottom-up and top-down signals. Occluded parts of a pattern are restored mainly by feedback signals from the highest stage of the network, while the unoccluded parts are reproduced mainly by signals from lower stages. The model does not use a simple template matching method. It can recognize and restore even deformed versions of learned patterns.