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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Vydané v:	Computer graphics forum Ročník 34; číslo 3; s. 111 - 120
Hlavní autori:	Soundararajan, K. P., Schultz, T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.06.2015
Predmet:	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
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