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 |
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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PRMU2015-104 |
Conference Information |
Committee |
PRMU |
Conference Date |
2015-12-21 - 2015-12-22 |
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(See Japanese page) |
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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 |
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Binary features |
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deep learning |
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keypoint matching |
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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) |
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Hironobu Fujiyoshi |
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Chubu University (Chubu University) |
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Speaker |
Author-1 |
Date Time |
2015-12-21 15:10:00 |
Presentation Time |
60 minutes |
Registration for |
PRMU |
Paper # |
PRMU2015-104 |
Volume (vol) |
vol.115 |
Number (no) |
no.388 |
Page |
pp.53-73 |
#Pages |
21 |
Date of Issue |
2015-12-14 (PRMU) |
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