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Accurate Computation of Interval Volume Measures for Improving Histograms

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

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Abstract

The interval volume measure is defined as the volume of the space occupied by a range of isosurfaces corresponding to an interval of isovalues. The interval volume measures are very useful since they can be taken as an alternative way of producing a smooth noise-suppressing histogram. This paper proposes two new methods (i.e., the subdividing method and the slicing method) that can calculate interval volume measures with very high accuracy for scalar regular-grid volumetric datasets. It is assumed that the underlying function inside the grid cell is defined by trilinear interpolation. A refined histogram method that can produce accurate interval volume measures is also presented in the paper. All three methods are compared against one another in terms of accuracy and performance. Their improvement for computing global and local histograms is demonstrated by comparing against the previous methods.

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Author information

Authors and Affiliations

  1. School of Science and Technology, Georgia Gwinnett College, Lawrenceville, USA

    Cuilan Wang

Authors
  1. Cuilan Wang
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Correspondence to Cuilan Wang .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Wang, C. (2019). Accurate Computation of Interval Volume Measures for Improving Histograms. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_24

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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