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Proceedings Paper

A novel improved CNN algorithm via denoising approach
Author(s): Jie Zhang; Jingjing Liu; Mingyu Wang; Aiying Guo; Yuyao Xiao
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Paper Abstract

Address-event-based Dynamic Vision Sensor(DVS) and Convolutional Neural Network(CNN) have been widely researched in recent years. However, the collected data of DVS are easily affected by some noise, which makes it difficult to identify the target during the classification processing. In order to solve the problem of misclassification, a novel improved CNN(NI-CNN) technique is proposed in this paper. Firstly, the appropriate number of event pulses are chosen and mapped to the frame domain, then the optimization denosing approach is utilized to the whole classification system. Secondly, reducing intra-class spacing and enlarging inter-class divergence by joint loss function which is adjusted regularization parameters. Numerical comparisons between our proposed approach and some state-of-the-art solvers, on several accessible databases, are presented to demonstrate its efficiency and effectiveness.

Paper Details

Date Published: 27 November 2019
PDF: 8 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113212G (27 November 2019);
Show Author Affiliations
Jie Zhang, Fudan Univ. (China)
Jingjing Liu, Fudan Univ. (China)
Shanghai Univ. of Electrical Power (China)
Mingyu Wang, Fudan Univ. (China)
Aiying Guo, Shanghai Univ. (China)
Yuyao Xiao, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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