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Visual Analysis of 3D Data by Isovalue Clustering

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

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Abstract

Visualization of volumetric data is ubiquitous in data analysis and has been widely used for exploration in scientific simulations and biomedical imaging. While direct and indirect visualization algorithms are employed extensively in applications, the visual exploration of features in the volumetric data is still a laborious task. We present an algorithm to extract exemplar isosurfaces from a 3D scalar field data set and provide the user with a representative visualization of the data. The presented approach provides an interactive tool that aids in visual analysis and exploration tasks. Our experiments on a number of benchmark data sets suggest that, compared to existing methods, the proposed approach provides a more distinct set of isosurfaces that are more representative of the complexity of the data sets.

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Author information

Authors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Susanne K. Suter, Bo Ma & Alireza Entezari

Authors
  1. Susanne K. Suter
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  2. Bo Ma
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  3. Alireza Entezari
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Suter, S.K., Ma, B., Entezari, A. (2014). Visual Analysis of 3D Data by Isovalue Clustering. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_30

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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