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.
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Publications
1.1K
Citations
80
Authors
38
Institutions
Publications per year
2017–2026
11
Parents
22
Spatialized Transfer Functions
Stefan Roettger, Michael Bauer, Marc Stamminger · Eurographics · 2005 · 103 citations · Full text
Computing, Radiology, Visualization +17
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University of Münster
Münster, Germany
2 h-index
8 publications
376 citations
Ministry of Education of the People's Republic of China
Beijing, China
2 h-index
4 publications
77 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
8 publications
156 citations
Tongji University
Shanghai, China
2 h-index
5 publications
149 citations

Henan University of Technology
Zhengzhou, China
1 h-index
1 publications
35 citations

Lindholmen Science Park
Gothenburg, Sweden
1 h-index
1 publications
21 citations

Xi'an Jiaotong University
Xi'an, China
1 h-index
4 publications
324 citations
Institute of Experimental Physics of the Slovak Academy of Sciences
Košice, Slovakia
1 h-index
1 publications
23 citations
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TVCG · 4 publications · 244 citations
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Repository · 3 publications · 163 citations
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1 publications · 138 citations
1
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1 publications · 19 citations
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JAAS · 1 publications · 23 citations
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