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Paper Abstract and Keywords
Presentation 2014-12-04 15:45
Discoverying Appearance-based Grasp Structures with Wearable Cameras
Minjie Cai (Univ. of Tokyo), Kris M. Kitani (Carnegie Mellon Univ.), Yoichi Sato (Univ. of Tokyo) CNR2014-26
Abstract (in Japanese) (See Japanese page) 
(in English) Our goal is to automatically recognize hand grasps and to discover the visual structures (relationships) between hand grasp using wearable cameras. Wearable cameras provide a first-person perspective which enables continuous visual hand grasp analysis of everyday activities. In contrast to previous work focused on manual analysis of first-person videos of hand grasps, we propose a fully automatic vision-based approach for grasp analysis. A set of grasp classifiers are trained for discriminating between different grasp types based on large margin visual predictors. Building on the output of these grasp classifiers, visual structures among hand grasps are learned based on an iterative discriminative clustering procedure. We evaluated our approach on real-world data taken from a machinist. The average F1 score of our grasp classifiers achieves over 0.40 for the real-world grasp dataset. Analysis of real-world video shows that it is possible to automatically learn intuitive visual grasp structures that are consistent with expert-designed grasp taxonomies.
Keyword (in Japanese) (See Japanese page) 
(in English) Hand grasp / / / / Recognition / First-person vision / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 351, CNR2014-26, pp. 49-54, Dec. 2014.
Paper # CNR2014-26 
Date of Issue 2014-11-27 (CNR) 
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 CNR  
Conference Date 2014-12-04 - 2014-12-04 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To CNR 
Conference Code 2014-12-CNR 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Discoverying Appearance-based Grasp Structures with Wearable Cameras 
Sub Title (in English)  
Keyword(1) Hand grasp  
Keyword(5) Recognition  
Keyword(6) First-person vision  
1st Author's Name Minjie Cai  
1st Author's Affiliation The University of Tokyo (Univ. of Tokyo)
2nd Author's Name Kris M. Kitani  
2nd Author's Affiliation Carnegie Mellon University (Carnegie Mellon Univ.)
3rd Author's Name Yoichi Sato  
3rd Author's Affiliation The University of Tokyo (Univ. of Tokyo)
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Date Time 2014-12-04 15:45:00 
Presentation Time 30 
Registration for CNR 
Paper # IEICE-CNR2014-26 
Volume (vol) IEICE-114 
Number (no) no.351 
Page pp.49-54 
#Pages IEICE-6 
Date of Issue IEICE-CNR-2014-11-27 

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