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Paper Abstract and Keywords
Presentation 2016-01-28 15:00
Off-Chip Learning Algorithm for Hardware Hand-Sign Recognition System
Masayuki Tamaki, Hikawa Hiroomi (Kansai Univ) NC2015-57
Abstract (in Japanese) (See Japanese page) 
(in English) This paper discusses a new off-chip learning algorithm for hardware hand sign recognition system. The hand sign
recognition system consists of a feature vector extraction and a classification network. The classifier network consists of a
SOM and a Hebbian (SOM-Hebb) hybrid network. The hardware hand-sign recognition system is implemented on a field
programmable gate array (FPGA), which is connected to a CMOS camera. The hardware can perform the recognition at a
speed of 60 fps. The recognition algorithm is very robust against the location change of hand signs, but it is not immune to
rotation or scaling, which degrades recognition performance. In the previous work, it was demonstrated that its recognition
performance was improved by additive perturbation to the training data for the SOM-Hebb classifier. However, users have to
add perturbation because the training of the system is carried out by off-chip learning that uses feature vectors. In this paper,
additive perturbation to the feature vector, which is equivalent to scale perturbation to input image, is proposed, and a new
off-chip learning that adds the perturbation, is developed. The feasibility of the system is verified by experiments against 24
patterns of American sign language (ASL). The experimental results show that the system can run at 94.3% of recognition
rate.
Keyword (in Japanese) (See Japanese page) 
(in English) Hand Sign Recognition / Off-Chip Learning / pattern recognition / VHDL, / FPGA / / /  
Reference Info. IEICE Tech. Rep., vol. 115, no. 426, NC2015-57, pp. 7-12, Jan. 2016.
Paper # NC2015-57 
Date of Issue 2016-01-21 (NC) 
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 NC2015-57

Conference Information
Committee NC NLP  
Conference Date 2016-01-28 - 2016-01-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyushu Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Implementation of Neuro Computing,Analysis and Modeling of Human Science, etc 
Paper Information
Registration To NC 
Conference Code 2016-01-NC-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Off-Chip Learning Algorithm for Hardware Hand-Sign Recognition System 
Sub Title (in English)  
Keyword(1) Hand Sign Recognition  
Keyword(2) Off-Chip Learning  
Keyword(3) pattern recognition  
Keyword(4) VHDL,  
Keyword(5) FPGA  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Masayuki Tamaki  
1st Author's Affiliation Kansai University (Kansai Univ)
2nd Author's Name Hikawa Hiroomi  
2nd Author's Affiliation Kansai University (Kansai Univ)
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Speaker Author-1 
Date Time 2016-01-28 15:00:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2015-57 
Volume (vol) vol.115 
Number (no) no.426 
Page pp.7-12 
#Pages 6 
Date of Issue 2016-01-21 (NC) 

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