Restoring partly occluded patterns: a neural network model.
This paper proposes a neural network model that has an ability to restore missing portions of partly occluded patterns. It is a multi-layered hierarchical neural network, in which visual information is processed by interaction of bottom-up and top-down signals. Memories of learned patterns are stored in the connections between cells. Occluded parts of a pattern are reconstructed mainly by top-down signals from higher stages of the network, while the unoccluded parts are reproduced mainly by signals from lower stages. The restoration progresses successfully, even if the occluded pattern is a deformed version of a learned pattern. The model tries to complete even an unlearned pattern by interpolating and extrapolating visible edges. Resemblance of local features to other learned patterns are also utilized for the restoration.
Fukushima
Kunihiko
K
School of Media Science, Tokyo University of Technology, 1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan. fukushima@media.teu.ac.jp
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010363:Pattern Recognition, Automated