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Paper Abstract and Keywords
Presentation 2019-12-20 14:55
Depth from Focal Stack by Deep Neural Network
Chen Baihui, Takahashi Keita, Fujii Toshiaki (Nagoya Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) Light field theory is a heated topic in computer vision. Focal stack is one of the one of the most significant characteristics of light field. However, in most learning methods, less attention was paid on focal stack. Therefore, we put forward depth estimation of light field image by machine learning method using focal stack. We extract depth information and performed by depth map. In experiment, we use deep neural network and a complete dataset for training, and then make some evaluations.
Keyword (in Japanese) (See Japanese page) 
(in English) light field / focal stack / depth estimation / neural network / / / /  
Reference Info. ITE Tech. Rep., vol. 43, no. 43, 3DIT2019-37, pp. 17-19, Dec. 2019.
Paper # 3DIT2019-37 
Date of Issue 2019-12-13 (IST, IDY, 3DIT) 
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Committee 3DIT IDY IST  
Conference Date 2019-12-20 - 2019-12-20 
Place (in Japanese) (See Japanese page) 
Place (in English) NHK Nagoya Station 
Topics (in Japanese) (See Japanese page) 
Topics (in English) 3D Imaging, Hyper-Realistic Imaging, etc. 
Paper Information
Registration To 3DIT 
Conference Code 2019-12-3DIT-IDY-IST 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Depth from Focal Stack by Deep Neural Network 
Sub Title (in English)  
Keyword(1) light field  
Keyword(2) focal stack  
Keyword(3) depth estimation  
Keyword(4) neural network  
1st Author's Name Chen Baihui  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Takahashi Keita  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Fujii Toshiaki  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
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Date Time 2019-12-20 14:55:00 
Presentation Time 25 
Registration for 3DIT 
Paper # ITE-IST2019-62,ITE-IDY2019-62,ITE-3DIT2019-37 
Volume (vol) ITE-43 
Number (no) no.43 
Page pp.17-19 
#Pages ITE-3 
Date of Issue ITE-IST-2019-12-13,ITE-IDY-2019-12-13,ITE-3DIT-2019-12-13 

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