Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers

Complex volume rendering tasks require high‐dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn transfer functions from scribbles that the user places in the volumetric domain in an intuitive and natural manner. In this paper, we explicitly mod...

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Veröffentlicht in:	Computer graphics forum Jg. 34; H. 3; S. 111 - 120
Hauptverfasser:	Soundararajan, K. P., Schultz, T.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Blackwell Publishing Ltd 01.06.2015
Schlagworte:	Analysis Artificial intelligence Categories and Subject Descriptors (according to ACM CCS) Classification Classifiers Forests I.4.6 [Image Processing and Computer Vision]: Segmentation-Pixel classification Image processing systems Mathematical models Probabilistic methods Probability theory Rendering Studies Transfer functions
ISSN:	0167-7055, 1467-8659
Online-Zugang:	Volltext
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