| Project/Area Number |
11610070
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
実験系心理学
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| Research Institution | Ochanomizu University |
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Principal Investigator |
ISHIGUCHI Akira Faculty of Letters and Education, Ochanomizu University, Professor, 文教育学部, 教授 (10184508)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2000: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1999: ¥2,300,000 (Direct Cost: ¥2,300,000)
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| Keywords | ideal observer / neural network / efficiency / visual information processing / model / 両眼立体視 / アルゴリズム / 方位検出 |
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| Research Abstract |
The purpose of this study was as follows : First, we calculate a statistical efficiency which reflects relative discriminability of the human visual system to that of an ideal observer in order to investigate some properties in lower to higher processes of the visual system. Secondly, we construct a neural network model of the ideal observer by learning a statistically optimal responses in visual tasks. Furthermore, we artificially disorder the network with use of an algorithm and make it to simulate the human performance.
We obtained the following results :
(1) We calculated the statistical efficiency in the task of detecting symmetry patterns on the three dimensional (3D) corrugate surface with depth noise. With using these results, we could build a model about a surface construction and pattern detection in it.
(2) We studied a problem about an integration of motion parallax with binocular disparity in the 3D slant perception with use of measurement of the statistical efficiency. We made clear the sampling differences between the motion parallax and binocular disparity information.
(3) We studied a spatial and temporal summation of orientations of lines with use of measurements of the statical efficiency, and clarified the mechanism of the temporal sampling superiority over the spatial sampling.
(4) We constructed an learning algorithm which was a basis of the neural network model of the ideal observer.
(5) With use of the above algorithm, we built a double-steps neural network model of the ideal observer in the structure-from-motion task.
(6) We made an algorithm which causes some disorder in the network and simulates a human performance.
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