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Paper Abstract and Keywords
Presentation 2018-02-16 10:15
Forked Object Detector for Manga Objects
Toru Ogawa, Toshihiko Yamasaki, Kiyoharu Aizawa (UT)
Abstract (in Japanese) (See Japanese page) 
(in English) Comics play an important role in both the entertainment and the cultural heritage. With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there is a problem in applying these methods to the comic object detection task. The problem is that the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve this problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods and confirmed that our model outperformed them based on the mAP score.
Keyword (in Japanese) (See Japanese page) 
(in English) comics / object detection / / / / / /  
Reference Info. ITE Tech. Rep., vol. 42, no. 4, ME2018-30, pp. 293-298, Feb. 2018.
Paper # ME2018-30 
Date of Issue 2018-02-08 (MMS, HI, ME, AIT) 
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Conference Date 2018-02-15 - 2018-02-16 
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 2018-02-ITS-IE-MMS-HI-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Forked Object Detector for Manga Objects 
Sub Title (in English)  
Keyword(1) comics  
Keyword(2) object detection  
1st Author's Name Toru Ogawa  
1st Author's Affiliation The University of Tokyo (UT)
2nd Author's Name Toshihiko Yamasaki  
2nd Author's Affiliation The University of Tokyo (UT)
3rd Author's Name Kiyoharu Aizawa  
3rd Author's Affiliation The University of Tokyo (UT)
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Date Time 2018-02-16 10:15:00 
Presentation Time 15 
Registration for ME 
Paper # ITE-MMS2018-30,ITE-HI2018-30,ITE-ME2018-30,ITE-AIT2018-30 
Volume (vol) ITE-42 
Number (no) no.4 
Page pp.293-298 
#Pages ITE-6 
Date of Issue ITE-MMS-2018-02-08,ITE-HI-2018-02-08,ITE-ME-2018-02-08,ITE-AIT-2018-02-08 

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