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Paper Abstract and Keywords
Presentation 2021-02-18 14:50
A Note on Estimation of Deteriorated Regions Based on Anomaly Detection from Rubber Material Electron Microscope Images -- Verification of Feature Representations Extracted from Deep Learning Models --
Masanao Matsumoto, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents an anomaly detection method for estimation of deteriorated regions from rubber material electron microscope images. In order to develop rubber materials with high durability, it is important to clarify the cause of deterioration. For analyzing the cause of deterioration, it is expected to utilize machine learning technology, especially deep learning. Although deterioration of the rubber materials can be observed from electron microscope images, it is difficult to obtain a large number of deteriorated data. Hence, we solve the above problem by using feature representations based on deep learning. Deep convolutional neural network (DCNN) can learn high representation features from target data sources, and extracted features from pre-trained DCNNs have been used by many researchers. In this paper, we can obtain features based on deep learning from rubber materials by using such pre-trained DCNNs. Finally, we can estimate deteriorated regions based on anomaly detection by using the obtained features. In this paper, we verify feature representations extracted from DCNN models to improve the estimation performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Region estimation / Anomaly detection / Deep learning / Feature representation / Rubber materials / / /  
Reference Info. ITE Tech. Rep., vol. 45, no. 4, ME2021-9, pp. 43-46, Feb. 2021.
Paper # ME2021-9 
Date of Issue 2021-02-11 (MMS, ME, AIT) 
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Committee IEICE-IE IEICE-ITS MMS ME AIT  
Conference Date 2021-02-18 - 2021-02-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2021-02-IE-ITS-MMS-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Estimation of Deteriorated Regions Based on Anomaly Detection from Rubber Material Electron Microscope Images 
Sub Title (in English) Verification of Feature Representations Extracted from Deep Learning Models 
Keyword(1) Region estimation  
Keyword(2) Anomaly detection  
Keyword(3) Deep learning  
Keyword(4) Feature representation  
Keyword(5) Rubber materials  
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1st Author's Name Masanao Matsumoto  
1st Author's Affiliation Hokkaido University (Hokkaido Univ)
2nd Author's Name Ren Togo  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ)
3rd Author's Name Takahiro Ogawa  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ)
4th Author's Name Miki Haseyama  
4th Author's Affiliation Hokkaido University (Hokkaido Univ)
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Speaker
Date Time 2021-02-18 14:50:00 
Presentation Time 25 
Registration for ME 
Paper # ITE-MMS2021-9,ITE-ME2021-9,ITE-AIT2021-9 
Volume (vol) ITE-45 
Number (no) no.4 
Page pp.43-46 
#Pages ITE-4 
Date of Issue ITE-MMS-2021-02-11,ITE-ME-2021-02-11,ITE-AIT-2021-02-11 


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