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Paper Abstract and Keywords
Presentation 2022-01-27 16:05
Proposal and evaluation of 3D-point object estimation method based on probability space representation
Hiroaki Sato, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) CQ2021-83
Abstract (in Japanese) (See Japanese page) 
(in English) New network services are expected to emerge using real spatial information in remote areas. For the advancement of services, it is important to understand real space from real spatial information, and for this purpose, object identification technology using machine learning is being introduced. In this paper, we have tried to represent real space as a field of probabilistic superposition of objects, instead of directly identifying real space information using machine learning techniques. We obtain neighboring information based on the positional relationships of objects in real space from a dataset used by the machine learning. Then, we propose a method of estimating objects by adding empirical knowledge information of real space to the object identification results. Our results show that predictions are changed by adapted empirical knowledge information and the estimation accuracy is improved when the confidence by the machine learning is not high.
Keyword (in Japanese) (See Japanese page) 
(in English) point cloud / random field representation / object detection / deep learning / semantic segmentation / Bayesian estimation / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 357, CQ2021-83, pp. 39-44, Jan. 2022.
Paper # CQ2021-83 
Date of Issue 2022-01-20 (CQ) 
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 CQ2021-83

Conference Information
Committee CQ CBE  
Conference Date 2022-01-27 - 2022-01-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Kanazawa（Ishikawa Pref.） 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AR/VR, Broadcasting Service, Video/Voice Services Quality, High Realistic, User Behavior/Psychology, User Experience, Media Quality, Network Quality and QoS Control, Networks and Communications at Disaster, User Behavior, Machine Learning, Video Communication, etc. 
Paper Information
Registration To CQ 
Conference Code 2022-01-CQ-CBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Proposal and evaluation of 3D-point object estimation method based on probability space representation 
Sub Title (in English)  
Keyword(1) point cloud  
Keyword(2) random field representation  
Keyword(3) object detection  
Keyword(4) deep learning  
Keyword(5) semantic segmentation  
Keyword(6) Bayesian estimation  
Keyword(7)  
Keyword(8)  
1st Author's Name Hiroaki Sato  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Shin'ichi Arakawa  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Masayuki Murata  
3rd Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2022-01-27 16:05:00 
Presentation Time 25 minutes 
Registration for CQ 
Paper # CQ2021-83 
Volume (vol) vol.121 
Number (no) no.357 
Page pp.39-44 
#Pages 6 
Date of Issue 2022-01-20 (CQ) 

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