Paper Abstract and Keywords |
Presentation |
2020-12-17 14:55
Improving the accuracy of unsupervised segmentation by introducing a Laplacian filter loss function
-- Application to automotive wire harness components -- Yuki Matsumoto (SEI) PRMU2020-45 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Semantic segmentation, in which images are classified into pixel-by-pixel classes by deep learning, has been widely studied to solve various social problems such as automated driving and medical imaging, and has been applied to the visual inspection of industrial products. In this paper, we investigated the performance of unsupervised learning in segmentation of industrial products where large datasets with annotations are difficult to collect, and furthermore, we proposed a loss function using a Laplacian filter to improve the accuracy of segmentation near the boundary of the inspected object. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Unsupervised learning / Semantic segmentation / Laplacian filter / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-45, pp. 42-46, Dec. 2020. |
Paper # |
PRMU2020-45 |
Date of Issue |
2020-12-10 (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) |
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PRMU2020-45 |
Conference Information |
Committee |
PRMU |
Conference Date |
2020-12-17 - 2020-12-18 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Transfer learning and few shot learning |
Paper Information |
Registration To |
PRMU |
Conference Code |
2020-12-PRMU |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Improving the accuracy of unsupervised segmentation by introducing a Laplacian filter loss function |
Sub Title (in English) |
Application to automotive wire harness components |
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Unsupervised learning |
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Semantic segmentation |
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Laplacian filter |
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1st Author's Name |
Yuki Matsumoto |
1st Author's Affiliation |
Sumitomo Electric Industries, Ltd (SEI) |
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Speaker |
Author-1 |
Date Time |
2020-12-17 14:55:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2020-45 |
Volume (vol) |
vol.120 |
Number (no) |
no.300 |
Page |
pp.42-46 |
#Pages |
5 |
Date of Issue |
2020-12-10 (PRMU) |
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