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Paper Abstract and Keywords
Presentation 2022-03-08 13:50
An Experimental Study on Estimating Residual Quantity of Foodstuff based on Deep Learning using 3D Model
Hiromu Takata, Syuhei Sato, Shangce Gao, Zheng Tang (Univ. Of Toyama)
Abstract (in Japanese) (See Japanese page) 
(in English) With the recent development of deep learning techniques, many image recognition methods have been proposed for various objects including foods and ingredients. However, preparing training datasets of real objects is difficult, because foods and ingredients often deteriorate quickly and a number of those types are large. Therefore, we have been studying a deep learning-based image recognition method which uses 3D models as training dataset. In this paper, we focus on an estimation of remaining amount of food ingredients, and we conduct an experiment on a simple sphere as an initial step of our study, and report its result.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / image recognition / food ingredients / 3D model / / / /  
Reference Info. ITE Tech. Rep., vol. 46, pp. 393-394, March 2022.
Paper #  
Date of Issue 2022-03-01 (AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Conference Date 2022-03-08 - 2022-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Expressive Japan 2022 
Paper Information
Registration To IIEEJ 
Conference Code 2022-03-AIT-IIEEJ-AS-ARTS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Experimental Study on Estimating Residual Quantity of Foodstuff based on Deep Learning using 3D Model 
Sub Title (in English)  
Keyword(1) deep learning  
Keyword(2) image recognition  
Keyword(3) food ingredients  
Keyword(4) 3D model  
1st Author's Name Hiromu Takata  
1st Author's Affiliation University Of Toyama (Univ. Of Toyama)
2nd Author's Name Syuhei Sato  
2nd Author's Affiliation University Of Toyama (Univ. Of Toyama)
3rd Author's Name Shangce Gao  
3rd Author's Affiliation University Of Toyama (Univ. Of Toyama)
4th Author's Name Zheng Tang  
4th Author's Affiliation University Of Toyama (Univ. Of Toyama)
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Speaker Author-1 
Date Time 2022-03-08 13:50:00 
Presentation Time 90 minutes 
Registration for IIEEJ 
Paper # AIT2022-147 
Volume (vol) vol.46 
Number (no) no.10 
Page pp.393-394 
Date of Issue 2022-03-01 (AIT) 

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