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Information-theoretic exploration for texture-based visualization

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Abstract

In this paper, we present a novel texture-based visualizing algorithm. This algorithm can be used as a robust and effective descriptor based on view-dependent information-theoretic statistical analysis of flow data. We calculate local entropy values to measure the distinct feature structures in the underlying field. Volume rendering is used to visualize the extracted flow patterns automatically. Due to the high computational expense, texture construction and rendering skip all empty and unimportant blocks. And an efficient GPU implementation is integrated together for high interactive frame rates. We show the key components of our approach with detailed analysis, and demonstrate that our method can visualize 2D and 3D flow data effectively.

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Acknowledgments

The authors would like to thank the reviewers for their valuable comments. Thanks to Prof. Roger Crawfis from Ohio State University for kindly providing the Tornado data set, and thanks to Wang Z.F. from the Institute of Atmospheric Physics in Chinese Academy of Sciences for providing the atmospheric pollution data set in Pearl River Delta region. This work is supported and funded by the National Natural Science Foundation of China (No. 61173067, No. 61272322, No. 61303157, No. 61379085).

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Authors and Affiliations

  1. College of Software, Qufu Normal University, Qufu, 273165, Shandong Province, People’s Republic of China

    Daying Lu

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, People’s Republic of China

    Daying Lu

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  1. Daying Lu
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Correspondence to Daying Lu.

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Lu, D. Information-theoretic exploration for texture-based visualization. J Vis 20, 393–404 (2017). https://doi.org/10.1007/s12650-016-0376-4

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