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Paper Abstract and Keywords
Presentation 2022-09-22 16:20
Image Classification Using Neural Network for Feature Extractable CMOS Image Sensor
Kohei Yamamoto, Kota Yoshida, Shunsuke Okura (Ritsumei Univ)
Abstract (in Japanese) (See Japanese page) 
(in English) CMOS image sensors are expected to be integrated with AI using deep learning for new technologies. Our research group has been studying feature-extractable pixels for an event-driven CMOS image sensor. In our previous work, it is found that object detection accuracy of people was low as around 17% in the object detection of feature images using a neural network trained with normal color images. In this work, we focus on the simple classification of feature images, and verified the classification accuracy of feature images with a neural network trained with feature images. Furthermore, to reduce redundant data contained in feature images to reduce the power consumption of the image recognition system, we verified the effect of bit depth of the feature image on the recognition accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) CMOS / Feature Extraction / Neural Network / Histogram Equalization / Sigmoid Function / / /  
Reference Info. ITE Tech. Rep., vol. 46, no. 29, IST2022-39, pp. 21-24, Sept. 2022.
Paper # IST2022-39 
Date of Issue 2022-09-15 (IST) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Committee IST  
Conference Date 2022-09-22 - 2022-09-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IST 
Conference Code 2022-09-IST 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Image Classification Using Neural Network for Feature Extractable CMOS Image Sensor 
Sub Title (in English)  
Keyword(1) CMOS  
Keyword(2) Feature Extraction  
Keyword(3) Neural Network  
Keyword(4) Histogram Equalization  
Keyword(5) Sigmoid Function  
1st Author's Name Kohei Yamamoto  
1st Author's Affiliation Ritsumeikan University (Ritsumei Univ)
2nd Author's Name Kota Yoshida  
2nd Author's Affiliation Ritsumeikan University (Ritsumei Univ)
3rd Author's Name Shunsuke Okura  
3rd Author's Affiliation Ritsumeikan University (Ritsumei Univ)
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Speaker Author-1 
Date Time 2022-09-22 16:20:00 
Presentation Time 30 minutes 
Registration for IST 
Paper # IST2022-39 
Volume (vol) vol.46 
Number (no) no.29 
Page pp.21-24 
Date of Issue 2022-09-15 (IST) 

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