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Paper Abstract and Keywords
Presentation 2019-12-12 13:00
Detecting dish images from comics using deep learning
Tatsuya Kobayashi, Mitsunori Matsushita (Kansai Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, it has been studied that various methods for detecting objects depicted in a comic by learning comic images. With conventional methods, however, the learning data used to detect objects in the comics is line drawings, and the amount of data sets for that purpose is limited. To solve this problem, this study examines how to detect dishes depicted in comics by learning actual cooking photos with a deep learning method. Our pro- posed method uses three types of cooking images such as (1) unprocessed (color), (2) gray scale, and (3) comic-style image. In our method, three types of cooking images were prepared: (1) unprocessed (color), (2) gray-scale, and (3) comic-like imaging. The objects in comics were detected by SSD300. This paper compared the accuracy of cooking image extraction in comics using this method.
Keyword (in Japanese) (See Japanese page) 
(in English) comics / image processing / detecting of object / dish images / / / /  
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Conference Information
Committee HCGSYMPO  
Conference Date 2019-12-11 - 2019-12-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima-ken Joho Plaza (Hiroshima) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) HCG Symposium 2019 
Paper Information
Registration To HCGSYMPO 
Conference Code 2019-12-HCGSYMPO 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Detecting dish images from comics using deep learning 
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Keyword(1) comics  
Keyword(2) image processing  
Keyword(3) detecting of object  
Keyword(4) dish images  
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1st Author's Name Tatsuya Kobayashi  
1st Author's Affiliation Kansai University (Kansai Univ.)
2nd Author's Name Mitsunori Matsushita  
2nd Author's Affiliation Kansai University (Kansai Univ.)
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Speaker Author-1 
Date Time 2019-12-12 13:00:00 
Presentation Time 15 minutes 
Registration for HCGSYMPO 
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