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Paper Abstract and Keywords
Presentation 2017-01-19 18:10
Fish Detection and Recognition for AR-based Learning Support System in an Aquarium
Chihiro Dogo (TUS), Tatsuya Kobayashi, Masaru Sugano (KDDI Research), Seiichiro Hangai (TUS) PRMU2016-139 MVE2016-30
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents fish detection and recognition method for AR-based learning support system, which helps users to learn about fishes swimming in an aquarium. The proposed method analyzes pictures of a water tank taken from the outside of the tank, and detects particular types of fish trained beforehand. The existing detection and recognition method lacks precision for practical use. Therefore, we apply automatic blurred image generation of training images, background subtraction and Kalman filtering to the conventional method. Experimental results show the improvement of detection accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Learning Support System / Deep Learning / Generic Object Recognition / Augmented Reality / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 412, MVE2016-30, pp. 165-169, Jan. 2017.
Paper # MVE2016-30 
Date of Issue 2017-01-12 (PRMU, MVE) 
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)
Download PDF PRMU2016-139 MVE2016-30

Conference Information
Committee PRMU IPSJ-CVIM MVE  
Conference Date 2017-01-19 - 2017-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MVE 
Conference Code 2017-01-PRMU-CVIM-MVE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Fish Detection and Recognition for AR-based Learning Support System in an Aquarium 
Sub Title (in English)  
Keyword(1) Learning Support System  
Keyword(2) Deep Learning  
Keyword(3) Generic Object Recognition  
Keyword(4) Augmented Reality  
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1st Author's Name Chihiro Dogo  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Tatsuya Kobayashi  
2nd Author's Affiliation KDDI Research, Inc. (KDDI Research)
3rd Author's Name Masaru Sugano  
3rd Author's Affiliation KDDI Research, Inc. (KDDI Research)
4th Author's Name Seiichiro Hangai  
4th Author's Affiliation Tokyo University of Science (TUS)
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Speaker Author-1 
Date Time 2017-01-19 18:10:00 
Presentation Time 25 minutes 
Registration for MVE 
Paper # PRMU2016-139, MVE2016-30 
Volume (vol) vol.116 
Number (no) no.411(PRMU), no.412(MVE) 
Page pp.165-169 
#Pages 5 
Date of Issue 2017-01-12 (PRMU, MVE) 

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