Paper Abstract and Keywords |
Presentation |
2021-03-04 13:30
[Poster Presentation]
Object Boundary Correction for Monocular Depth Estimation Using Region Segmentation Ikuma Yasukawa, Shoko Imaizumi (Chiba Univ.) EMM2020-67 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose a new method to generate high-quality depth images in this paper. The proposed method corrects object boundaries in a depth image derived from the monocular image using deep learning.A large amount of depth data is required for depth estimation with deep learning. However, it is difficult to fully prepare the depth data with the same size as the original monocular image. Therefore, a depth estimation method by self-supervised learning, which introduces stereo video sequences as training data, has been proposed.This method utilizes disparities of stereo video for learning, and thus the object boundaries in the estimated depth image tend to be ambiguous. In contrast, the proposed method first generates a background depth image without target objects and an object depth image of the target objects separately, and then combines both depth images. We evaluate the quality of the depth images obtained by the proposed and conventional methods and confirm the effectiveness of our method. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Depth estimation / boundary correction / segmentation / inpainting / multi-view image generation / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 418, EMM2020-67, pp. 1-6, March 2021. |
Paper # |
EMM2020-67 |
Date of Issue |
2021-02-25 (EMM) |
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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EMM2020-67 |
Conference Information |
Committee |
EMM |
Conference Date |
2021-03-04 - 2021-03-05 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. |
Paper Information |
Registration To |
EMM |
Conference Code |
2021-03-EMM |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Object Boundary Correction for Monocular Depth Estimation Using Region Segmentation |
Sub Title (in English) |
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Depth estimation |
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boundary correction |
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segmentation |
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inpainting |
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multi-view image generation |
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1st Author's Name |
Ikuma Yasukawa |
1st Author's Affiliation |
Chiba University (Chiba Univ.) |
2nd Author's Name |
Shoko Imaizumi |
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Chiba University (Chiba Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-03-04 13:30:00 |
Presentation Time |
15 minutes |
Registration for |
EMM |
Paper # |
EMM2020-67 |
Volume (vol) |
vol.120 |
Number (no) |
no.418 |
Page |
pp.1-6 |
#Pages |
6 |
Date of Issue |
2021-02-25 (EMM) |
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