A view-dependent spatiotemporal saliency-driven approach for time varying volumetric data in geovisualization
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- 作者：Jing Lia ; Jing.Li145@du.edu" class="auth_mail" title="E-mail the corresponding author ; Tong Zhangb ; c ; David W.S. Wongd ; Meghan Mooneya
- 关键词：Remote visualization ; Spatiotemporal saliency ; Dust storm data ; Cloud computing
- 刊名：Computers, Environment and Urban Systems
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：59
- 期：Complete
- 页码：64-77
- 全文大小：3007 K
文摘
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A novel measurement for spatiotemporal information saliency is proposed to quantify information diversity.
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A saliency-driven time–space partition tree (TSP) based visualization enhancement approach is designed.
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The saliency-driven approach is implemented in a cloud-based visualization framework.