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More Usable V-EGI for Volumetric Dataset Registration

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

Enhancements to the Volume-based Extended Gaussian Image (V-EGI) registration are described. The first enhancement is the capability to recover positional difference (in additional to rotational difference) between volumetric datasets. The second, and most important, enhancement uses a multi-stage coarse-to-fine processing strategy to improve computational speed. That enhancement also incorporates an optimization scheme to enable the strategy to maintain accuracy. The third enhancement is a methodology that achieves a moderate degree of parallelism on current-generation multi-core CPUs. Results of application of these methodologies to multiple datassets are also presented.

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Authors and Affiliations

  1. University of Alabama in Huntsville, Huntsville, AL, 35899, USA

    Chun Dong & Timothy S. Newman

Authors
  1. Chun Dong
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  2. Timothy S. Newman
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Corresponding author

Correspondence to Chun Dong .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Dong, C., Newman, T.S. (2015). More Usable V-EGI for Volumetric Dataset Registration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_46

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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