Probabilistic Transfer Functions

Probabilistic Transfer Function is a volume visualization technique that extends traditional transfer functions by mapping statistical distributions of volumetric data to optical properties, thereby enabling the visual representation of uncertainty in scalar fields through probabilistic membership assignments.

24

Publications

1.2K

Citations

68

Authors

36

Institutions

Publications per year

2017–2026

4

Variants

Probabilistic Transfer Function

Authors

68

  1. TJ
    Timothy J. Dysart

    University of Notre Dame, United States

    3 publications · 93 citations

    3

    h-index

  2. JJ
    Jianhui Jiang

    Tongji University, China

    2 publications · 37 citations

    2

    h-index

  3. PM
    Peter M. Kogge

    University of Notre Dame, United States

    2 publications · 65 citations

    2

    h-index

  4. JK
    Joe Kniss

    University of Utah, United States

    2 publications · 131 citations

    2

    h-index

  5. JX
    Jie Xiao

    Zhejiang University of Technology, China

    2 publications · 37 citations

    2

    h-index

1–5 of 68

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 181 citations

    3

    h-index

  2. Microelectronics Journal

    2 publications · 142 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 1 publications · 10 citations

    1

    h-index

  4. 1

    h-index

  5. Computer Graphics Forum

    1 publications · 22 citations

    1

    h-index

1–5