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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: Huang, Gongxiang; * | Qu, Huimin
Affiliations: HuaShang College, Guangdong University of Finance & Economics, Guangzhou, Guangdong, China
Correspondence: [*] Corresponding author. Huang, Gongxiang, Hua Shang College, Guangdong University of Finance & Economics, Guangzhou 511300, Guangdong, China. E-mail: [email protected].
Abstract: The influence of COVID-19 causes a certain impact on data visualization and data fusion on the visual performance of illustration. Based on the development of illustration, this paper discusses the relationship between illustration, text information and media. This paper studies the feasibility of the combination of illustration and information visualization. In this paper, the interactive image segmentation and gridding methods are proposed. Then, the background theory and significance of flow field design are described, and the flow field generation method based on heat source diffusion is proposed. In this paper, the shadow of the topology of the convective field through the interaction input of the flow field design is analyzed, and then compared with the related work. In the visualization of flow field, based on the weighted distance field formed by the diffusion of heat source, a visualization method of stratified flow field line is proposed. Finally, the visualization method of stratified flow field is explained and its effect is demonstrated. Experimental data show that the information visualization method proposed in this paper can improve the efficiency and accuracy of illustration information extraction.
Keywords: Illustration, media, information visualization, weighted distance field, COVID-19
DOI: 10.3233/JIFS-189276
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8795-8803, 2020
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