Feature-based Transfer Functions

Feature-Based Transfer Functions is a method in volume visualization that assigns optical properties to volumetric data by identifying and enhancing semantic features through derived data attributes, distinguishing itself from direct or data-value-based mapping approaches by leveraging higher-level feature representations such as gradients, curvatures, or statistical measures for more meaningful visual abstraction.

22

Publications

1.1K

Citations

80

Authors

38

Institutions

Publications per year

2017–2026

11

Authors

80

  1. KH
    Klaus Hinrichs

    University of Münster, Germany

    2 publications · 94 citations

    2

    h-index

  2. JP
    Jörg-Stefan Praßni

    University of Münster, Germany

    2 publications · 94 citations

    2

    h-index

  3. TR
    Timo Ropinski

    University of Münster, Germany

    2 publications · 94 citations

    2

    h-index

  4. AE
    Alireza Entezari

    Monash University, Australia

    1 publications · 24 citations

    1

    h-index

  5. XW
    Xin Wang

    Jiangsu Normal University, China

    1 publications · 138 citations

    1

    h-index

1–5 of 80

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 4 publications · 244 citations

    4

    h-index

  2. Eurographics

    Repository · 3 publications · 163 citations

    3

    h-index

  3. Measurement

    1 publications · 138 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 19 citations

    1

    h-index

  5. Journal of Analytical Atomic Spectrometry

    JAAS · 1 publications · 23 citations

    1

    h-index

1–5