Restoring Partly Occluded Patterns: A Neural Network Model with Backward Paths
Restoring Partly Occluded Patterns: A Neural Network Model with Backward Paths
复制标题
恢复部分遮挡的模式：具有后向路径的神经网络模型
DOI：
10.1007/3-540-44989-2_47
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
K. Fukushima
中科院分区：
文献类型：
--
作者：
K. Fukushima
本文提出了一种神经网络模型，该模型具有恢复部分遮挡模式的丢失部分的能力。它是一个多层次的神经网络，其中视觉信息是由自下而上和自上而下的信号的相互作用处理。被遮挡的部分主要由来自网络最高层的反馈信号重建，而未被遮挡的部分主要由来自较低层的信号再现。该模型不使用简单的模板匹配方法。它甚至可以恢复学习模式的变形版本。
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.
影响因子：
6
作者：
Fukushima, K
通讯作者：
Fukushima, K