Paper Abstract and Keywords |
Presentation |
2019-02-20 10:15
A note on estimation of inspectors' visual attention using distress images of subway tunnels
-- Trial introduction of deep learning-based saliency prediction methods -- Ryota Saito, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This paper presents the first trial for estimating inspectors' visual attention of distress images in subway tunnels. The goal of this study is to realize a system for supporting efficient inspection of subway tunnels.
Since our work enables to estimate inspectors' visual attention and to reveal a meaningful area, more efficient inspection is expected. This paper shows the results of several methods of deep learning-based saliency prediction for distress images. In addition, we analyze the differences between predicted salient regions and gaze regions calculated from inspectors' eye gaze data. New knowledge is obtained by the analysis in order to build an estimation method of inspectors' visual attention. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
estimation of visual attention / eye tracking data / deep learning / subway tunnel / / / / |
Reference Info. |
ITE Tech. Rep., vol. 43, no. 5, ME2019-52, pp. 281-285, Feb. 2019. |
Paper # |
ME2019-52 |
Date of Issue |
2019-02-12 (MMS, HI, ME, AIT) |
ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
Download PDF |
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Conference Information |
Committee |
ME IEICE-IE IEICE-ITS MMS HI AIT |
Conference Date |
2019-02-19 - 2019-02-20 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Hokkaido Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Image Processing, etc. |
Paper Information |
Registration To |
ME |
Conference Code |
2019-02-ME-IE-ITS-MMS-HI-AIT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A note on estimation of inspectors' visual attention using distress images of subway tunnels |
Sub Title (in English) |
Trial introduction of deep learning-based saliency prediction methods |
Keyword(1) |
estimation of visual attention |
Keyword(2) |
eye tracking data |
Keyword(3) |
deep learning |
Keyword(4) |
subway tunnel |
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1st Author's Name |
Ryota Saito |
1st Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
2nd Author's Name |
Keisuke Maeda |
2nd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
3rd Author's Name |
Takahiro Ogawa |
3rd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
4th Author's Name |
Miki Haseyama |
4th Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
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Speaker |
Author-1 |
Date Time |
2019-02-20 10:15:00 |
Presentation Time |
15 minutes |
Registration for |
ME |
Paper # |
MMS2019-30, HI2019-30, ME2019-52, AIT2019-30 |
Volume (vol) |
vol.43 |
Number (no) |
no.5 |
Page |
pp.281-285 |
#Pages |
5 |
Date of Issue |
2019-02-12 (MMS, HI, ME, AIT) |