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Paper Abstract and Keywords
Presentation 2021-08-26 10:00
Unsupervised non-rigid alignment for multiple noisy images
Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida (Kyushu Univ.), Hiroyuki Sekiguchi (Kyoto Univ.), Takayuki Yagi (Luxonus), Imari Sato (NII), Ryoma Bise (Kyushu Univ.) PRMU2021-7
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a deep non-rigid alignment network that can simultaneously perform non-rigid alignment and noise decomposition of images despite severe noise and sparse errors. To address this challenging task, we introduce a low-rank loss in deep learning under the assumption that a batch of well-aligned and well-denoised images should be linearly correlated, and thus the matrix consisting the images should be low-lank. It allows us to remove the noise and corruption from input images in a self-supervised learning manner (i.e., t does not require any supervised data). In addition, we introduce a self-attention technique in order to aggregate the information about corruption from the batch of images. To the best of our knowledge, it is first attempt to introduce a low-rank loss for unsupervised deep alignment. Experiments using toy data and real medical image data demonstrate the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Unsupervised / Deep Learning / Alignment / Sparse complement / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 155, PRMU2021-7, pp. 1-6, Aug. 2021.
Paper # PRMU2021-7 
Date of Issue 2021-08-19 (PRMU) 
ISSN 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 PRMU2021-7

Conference Information
Committee PRMU  
Conference Date 2021-08-26 - 2021-08-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) CV/PR techniques for human-robot cooperation 
Paper Information
Registration To PRMU 
Conference Code 2021-08-PRMU 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unsupervised non-rigid alignment for multiple noisy images 
Sub Title (in English)  
Keyword(1) Unsupervised  
Keyword(2) Deep Learning  
Keyword(3) Alignment  
Keyword(4) Sparse complement  
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1st Author's Name Takanori Asanomi  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Kazuya Nishimura  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Heon Song  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
4th Author's Name Junya Hayashida  
4th Author's Affiliation Kyushu University (Kyushu Univ.)
5th Author's Name Hiroyuki Sekiguchi  
5th Author's Affiliation Luxonus (Kyoto Univ.)
6th Author's Name Takayuki Yagi  
6th Author's Affiliation Luxonus (Luxonus)
7th Author's Name Imari Sato  
7th Author's Affiliation NII (NII)
8th Author's Name Ryoma Bise  
8th Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2021-08-26 10:00:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-7 
Volume (vol) vol.121 
Number (no) no.155 
Page pp.1-6 
#Pages 6 
Date of Issue 2021-08-19 (PRMU) 

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