1996 Fiscal Year Final Research Report Summary
Research on Visual Pattern Recognition with Hierarchical Neural Networks
| Project/Area Number |
07408005
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Osaka University |
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Principal Investigator |
FUKUSHIMA Kunihiko Osaka University, Faculty of Engineering Science, Professor, 基礎工学部, 教授 (90218909)
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| Co-Investigator(Kenkyū-buntansha) |
OKADA Masato Computational Neurobiology Group Kawato Dynamic Brain Project Japan Science and, 川人学習動態脳プロジェクト・計算神経生理グループ, 研究員
SHOUNO Hayaru Osaka University, Faculty of Engineering Science, Research Associate, 基礎工学部, 助手 (50263231)
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| Project Period (FY) |
1995 – 1996
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| Keywords | Active pattern recognition / Visual pattern recognition / Neural network model / Selective attention / Binding problem / Parallel processing of shape and motion / Spatial memory / Neocognitron |
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| Research Abstract |
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.
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