Neural Network Model Restoring Partly Occluded Patterns

Abstract

Even the 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.