Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers

• Published in: Computer graphics forum Vol. 34; no. 3; pp. 111 - 120
• Main Authors: Soundararajan, K. P., Schultz, T.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.06.2015
• Subjects: 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

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...