Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers

  title={Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers},
  author={Krishna Prasad Soundararajan and Thomas Schultz},
  journal={Comput. Graph. Forum},
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 model and visualize the uncertainty in the resulting classification. To this end, we extend a previous intelligent system approach to volume rendering, and we systematically compare five supervised… CONTINUE READING

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  • T. SCHAUL, J. BAYER, +4 authors T. RÜCKSTIESS
  • Journal of Machine Learning Research
  • 2010

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