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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 / / / /  
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Conference Information
Committee ITS IE ITE-MMS ITE-HI ITE-ME ITE-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 ITE-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 ITE-ME 
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Volume (vol) vol.118 
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