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Issue title: Special section: Artificial Intelligence driven Big Data Analytics for COVID-19
Guest editors: Xiaolong Li
Article type: Research Article
Authors: Tingting, Lianga; * | Zhaoguo, Liua | Wenzhan, Wangb
Affiliations: [a] Henan Polytechnic University, Jiaozuo, Henan, China | [b] Nikon Image Instrument Sales (China) Co., Ltd, Tokyo, Japan
Correspondence: [*] Corresponding author. Liang Tingting, Henan Polytechnic University, Jiaozuo, Henan, 454000, China. E-mail: [email protected].
Abstract: The Covid-19 first occurs in Wuhan, China in December 2019. After that, the virus has spread all over the world and at the time of writing this paper the total number of confirmed cases are above 11 million with over 600,000 deaths. The pattern recognition of complex environment can be used to determine if a COVID-19 breath pattern can be established with accuracy. The traditional decorative pattern detection method has a high degree of recognition in simple scene. However, the efficiency of decorative pattern detection in complex scenes is low and the recognition accuracy is not high. Firstly, the evaluation index of target detection method is designed. Through this paper, it is found that the success rate of some targets is naturally better than other targets, and easy to distinguish from the background. In order to improve the recognition success rate of the object in the complex environment and determine the position and attitude of the object, the pattern as the artificial identification in the environment is proposed. The interior art decoration pattern is selected as the experimental pattern and the pattern classification evaluation index is designed. The experimental results show that the method proposed in this paper can optimize the pattern subsets which are confused with each other and easy to distinguish from the background. It has a certain reference value for decorative pattern recognition in complex environment for COVID-19 epidemic.
Keywords: Neural network, pattern classification, decorative pattern detection, complex environment, COVID-19
DOI: 10.3233/JIFS-189262
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8665-8673, 2020
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