This paper proposes a neural network model that has an ability to restore the 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. Occluded parts of a pattern are reconstructed 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 restore even deformed versions of learned patterns. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Fukushima, K. (2003). Restoring partly occluded patterns: A neural network model with backward paths. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 393–400. https://doi.org/10.1007/3-540-44989-2_47
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