2005 Fiscal Year Final Research Report Summary
Use of Top-Down Information for Visual Information Processing
| Project/Area Number |
14380169
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | Tokyo University of Technology |
|---|
Principal Investigator |
FUKUSHIMA Kunihiko Tokyo University of Technology, School of Media Science, Professor, メディア学部, 教授 (90218909)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
KIKUCHI Masayuki Tokyo University of Technology, School of Computer Science, Assistant Professor, コンピュータサイエンス学部, 講師 (20291437)
|
|---|
| Project Period (FY) |
2002 – 2005
|
| Keywords | Visual Information Processing / Neural network model / Pattern recognition / Occluded pattern / Incremental learning / Symmetry axis / Optic flow / Figure-ground separation |
|---|
| Research Abstract |
When we are looking at an object, we do not passively accept whole information within our visual field, but actively gather necessary information only. We focus our attention to the places that attract our interest. We capture information from there and process it selectively. We often try to predict a pattern using information from surrounding areas, and recognize it by confirming whether the initial prediction was correct. Top-down information plays an important role for such active processing of information.
Varieties of neurophysiological and psychological experimental results on higher brain functions, including top-down processing, have recently been reported. We tried to analyze these results systematically from the stand-point of information processing, and made modeling research to obtain new design principles for information processors of a new generation. Namely, we first propose a new model for a higher brain function, and improved the model so as to behave in a similar way as the biological brain. At the same time, we also made several experiments for practical implementation of the models to real-world problems.
As a result of these researches, we have obtained the following results :
(1)A model capable of recognizing and restoring partly occluded patterns
(2)Improving the recognition rate of the neocognitron (a model for robust visual pattern recognition)
(3)A new method for incremental learning appropriate for multi-layered neural network
(4)Use of blur for robust image processing --- a neural network model that extracts axes of symmetry from visual patterns
(5)Extraction of optic flow : A model of neural network for MT and MST cells
(6)Psychological experiments and models revealing relations among the mechanisms of figure-ground separation, contour integration, and motion integration.
(7)Relations among the perception for LPD stimuli, global motion integration, and transparent motion : psychological experiments and a computational model
|
