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Paper Abstract and Keywords
Presentation 2014-10-09 15:00
Learning Similarities for Rigid and Non-Rigid Object Detection
Asako Kanezaki (Univ. of Tokyo), Emanuele Rodola, Daniel Cremers (Technische Univ. Munchen), Tatsuya Harada (Univ. of Tokyo) PRMU2014-56
Abstract (in Japanese) (See Japanese page) 
(in English) We propose an optimization method for estimating parameters in graph-theoretical formulations of the matching problem for object detection. Unlike several methods which optimize parameters for graph matching in a way to promote correct correspondences and to restrict wrong ones, our approach aims at improving performance in the more general task of object detection. In our formulation, similarity functions are adjusted so as to increase the overall similarity among a reference model and the observed target, and at the same time reduce the similarity among reference and "non-target" objects. We evaluate the proposed method in two challenging scenarios, demonstrating substantial improvements in both settings.
Keyword (in Japanese) (See Japanese page) 
(in English) keypoint matching / graph matching / optimization / gradient descent method / 3D shape / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 230, PRMU2014-56, pp. 13-18, Oct. 2014.
Paper # PRMU2014-56 
Date of Issue 2014-10-02 (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)
Download PDF PRMU2014-56

Conference Information
Committee PRMU  
Conference Date 2014-10-09 - 2014-10-10 
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 2014-10-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Learning Similarities for Rigid and Non-Rigid Object Detection 
Sub Title (in English)  
Keyword(1) keypoint matching  
Keyword(2) graph matching  
Keyword(3) optimization  
Keyword(4) gradient descent method  
Keyword(5) 3D shape  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Asako Kanezaki  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Emanuele Rodola  
2nd Author's Affiliation Technische Universitat Munchen (Technische Univ. Munchen)
3rd Author's Name Daniel Cremers  
3rd Author's Affiliation Technische Universitat Munchen (Technische Univ. Munchen)
4th Author's Name Tatsuya Harada  
4th Author's Affiliation The University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2014-10-09 15:00:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2014-56 
Volume (vol) vol.114 
Number (no) no.230 
Page pp.13-18 
#Pages 6 
Date of Issue 2014-10-02 (PRMU) 

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