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Paper Abstract and Keywords
Presentation 2024-02-10 10:15
Examination of improvement in product region extraction based on deep neural network
Riku Kato, Takuya Futagami (AGU)
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a method, which can extract product regions at pixel-wise level from images uploaded to online market places. Based on LDA transformation, the proposed method creates effective features for GrabCut to improve the product extraction accuracy. To confirm an effectiveness of the proposed method, an experiment using 72 product images was performed. The results indicated that the proposed method increased the accuracy by 1.9% compared with the conventional product region extraction.
Keyword (in Japanese) (See Japanese page) 
(in English) Product region extraction / SegNet / Texture analysis / LDA / GrabCut / / /  
Reference Info. ITE Tech. Rep., vol. 48, no. 4, ME2024-3, pp. 8-11, Feb. 2024.
Paper # ME2024-3 
Date of Issue 2024-02-03 (ME) 
ISSN Online edition: ISSN 2424-1970
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Conference Information
Committee ME  
Conference Date 2024-02-10 - 2024-02-10 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To ME 
Conference Code 2024-02-ME 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Examination of improvement in product region extraction based on deep neural network 
Sub Title (in English)  
Keyword(1) Product region extraction  
Keyword(2) SegNet  
Keyword(3) Texture analysis  
Keyword(4) LDA  
Keyword(5) GrabCut  
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1st Author's Name Riku Kato  
1st Author's Affiliation Aichi Gakuin University (AGU)
2nd Author's Name Takuya Futagami  
2nd Author's Affiliation Aichi Gakuin University (AGU)
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Speaker Author-1 
Date Time 2024-02-10 10:15:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # ME2024-3 
Volume (vol) vol.48 
Number (no) no.4 
Page pp.8-11 
#Pages
Date of Issue 2024-02-03 (ME) 


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