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Research on Visual Pattern Recognition with Hierarchical Neural Networks

层次神经网络视觉模式识别研究

基本信息

批准号：
07408005
负责人：
FUKUSHIMA Kunihiko
金额：
$ 7.17万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (A)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07408005/
关键词：
Active pattern recognition Visual pattern recognition Neural network model Selective attention Binding problem Parallel processing of shape and motion Spatial memory Neocognitron 想起の連鎖広域地図の連続的想起競合学習特徴抽出のしきい値視覚神経系フィードバック信号

项目摘要

Aiming to develop new design principles for visual information processing systems of the next generation, we have concentrated our research on the active processes in the visual system of the biological brain. We used modeling approach to solve the mechanism of the brain, and proposed neural network models explaining various functions related to active vision. We also tried to design visual pattern recongnition systems using the results of the modeling research. We have performed various researches in parallel and have obtained the following results.(1) Neural network model that has two separate channels processing form and motion information. The model can solve the binding problem by the function of selective attention.(2) Neural network model of binocular cells. The model includes far-cells, near-cells, and fine-tuned cells. We also proposed a theory that can estimate the depth of an object occluded from one eye, and have shown that the results obtained from our theory coincide with the psychological experiments.(3) Eye movement model with non-uniform receptive fields.(4) Neural network model of spatial memory. The model memorizes the fragmentary maps of external world, and can recall a map of a wide area by a chain process of recalling.(5) Training neocognitron to recognize handwritten characters in the real world. The neocognitron, which we have developed previously, is a pattern recognition system whose architecture has been suggested from the mammalian visual system. We trained the neocognitron using a large-scale data base of handwritten digits (ETL-1), and obtained a recognition rate higher than 98%.(6) Theoretical analysis of the correlation matrix memory.

为了开发下一代视觉信息处理系统的新设计原则，我们集中研究了生物大脑视觉系统中的活动过程。我们使用建模的方法来解决大脑的机制，并提出了解释与主动视觉相关的各种功能的神经网络模型。我们还尝试利用建模研究的结果来设计视觉模式识别系统。我们并行开展了多方面的研究，取得了以下成果：(1)具有两个独立通道处理形式信息和运动信息的神经网络模型。该模型可以通过选择性注意的功能来解决绑定问题。(2)双眼细胞神经网络模型。该模型包括远单元、近单元和微调单元。我们还提出了一种可以估计单眼被遮挡物体的深度的理论，并证明了我们的理论得到的结果与心理学实验相吻合。(3)非均匀感受野的眼动模型。(4)空间记忆的神经网络模型。该模型对外部世界的零碎地图进行记忆，并通过一连串的回忆过程来回忆大范围的地图。(5)训练新认知者识别真实世界中的手写字符。我们之前开发的新认知电子管是一种模式识别系统，其体系结构是从哺乳动物的视觉系统中提出的。使用大规模手写数字数据库(ETL-1)对新认知网络进行训练，识别率达到98%以上。(6)相关矩阵记忆的理论分析。