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Paper Abstract and Keywords
Presentation 2015-12-21 15:10
Local feature description for keypoint matching
Mitsuru Ambai (Denso IT Laboratory, Inc.), Takahiro Hasegawa, Hironobu Fujiyoshi (Chubu University) PRMU2015-104
Abstract (in Japanese) (See Japanese page) 
(in English) A task of finding physically the sample points among multiple images captured from different viewpoints is called as key point matching for which discriminative local feature representation is very important. We categorize proposed methods in the past into three groups: (1) real-valued feature representation, (2) binary feature representation and (3) feature representation by deep learning, respectively. In this paper, we briefly overview the classic real-valued feature representation and especially focus on investigating the recent binary feature representation. In addition, we introduce a new trend that utilizes deep learning for nonlinear feature representation. Open source software, dataset and implementation techniques are also reviewed.
Keyword (in Japanese) (See Japanese page) 
(in English) Binary features / deep learning / keypoint matching / / / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 388, PRMU2015-104, pp. 53-73, Dec. 2015.
Paper # PRMU2015-104 
Date of Issue 2015-12-14 (PRMU) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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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Conference Information
Committee PRMU  
Conference Date 2015-12-21 - 2015-12-22 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2015-12-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Local feature description for keypoint matching 
Sub Title (in English)  
Keyword(1) Binary features  
Keyword(2) deep learning  
Keyword(3) keypoint matching  
1st Author's Name Mitsuru Ambai  
1st Author's Affiliation Denso IT Laboratory, Inc. (Denso IT Laboratory, Inc.)
2nd Author's Name Takahiro Hasegawa  
2nd Author's Affiliation Chubu University (Chubu University)
3rd Author's Name Hironobu Fujiyoshi  
3rd Author's Affiliation Chubu University (Chubu University)
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Date Time 2015-12-21 15:10:00 
Presentation Time 60 
Registration for PRMU 
Paper # IEICE-PRMU2015-104 
Volume (vol) IEICE-115 
Number (no) no.388 
Page pp.53-73 
#Pages IEICE-21 
Date of Issue IEICE-PRMU-2015-12-14 

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