Paper Abstract and Keywords |
Presentation |
2016-03-23 16:00
Classification of binocular disparities of a three-layered neural network with inputs of hybrid-type disparity detectors Ken Shibata, Hiroki Tanaka (Kyoto Sangyo Univ.) NC2015-89 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Binocular complex neurons in the primary visual cortex (V1) are successfully modeled as “hybrid-type” disparity-energy units whose receptive fields (RFs) contain both phase and position differences between the eyes. To understand the mechanisms for decoding stimulus binocular disparities using signals of these units, we constructed a three-layered neural network that included the hybrid-type units for the input layer and learned to classify binocular disparities (-0.5 to 0.5 degrees in 0.1 degree steps) in the random-dot stereograms. After learning, the hybrid-type network correctly (> 95%) classified the stimulus binocular disparities. Although the range of RF positional differences of the hybrid-type units (-0.7°~ 0.7°) is larger than that of the stimulus binocular disparities, nearly all of the middle-layer units are most strongly connected with the input-layer units whose RF phase difference is the maximum used in this experiment. The present results indicate that, for correct classification, the range of binocular disparities coded by the input layer should be considerably larger than that of stimulus binocular disparity. This may be one factor for the middle layer units being strongly connected with the hybrid-type units with large RF phase differences. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
binocular disparity / V1 / neural network / disparity energy model / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 115, no. 514, NC2015-89, pp. 113-117, March 2016. |
Paper # |
NC2015-89 |
Date of Issue |
2016-03-15 (NC) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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NC2015-89 |
Conference Information |
Committee |
MBE NC |
Conference Date |
2016-03-22 - 2016-03-23 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tamagawa University |
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(See Japanese page) |
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Paper Information |
Registration To |
NC |
Conference Code |
2016-03-MBE-NC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Classification of binocular disparities of a three-layered neural network with inputs of hybrid-type disparity detectors |
Sub Title (in English) |
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binocular disparity |
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V1 |
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neural network |
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disparity energy model |
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1st Author's Name |
Ken Shibata |
1st Author's Affiliation |
Kyoto Sangyo University (Kyoto Sangyo Univ.) |
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Hiroki Tanaka |
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Kyoto Sangyo University (Kyoto Sangyo Univ.) |
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Speaker |
Author-2 |
Date Time |
2016-03-23 16:00:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2015-89 |
Volume (vol) |
vol.115 |
Number (no) |
no.514 |
Page |
pp.113-117 |
#Pages |
5 |
Date of Issue |
2016-03-15 (NC) |
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