Volume Visualization based on Statistical Transfer-Function Spaces

Martin Haidacher, Daniel Patel, Stefan Bruckner, Armin Kanitsar, Meister Eduard Gröller
Volume Visualization based on Statistical Transfer-Function Spaces
In Proceedings of the IEEE Pacific Visualization 2010, pages 17-24. March 2010.
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Abstract

It is a difficult task to design transfer functions for noisy data. In traditional transfer-function spaces, data values of different materials overlap. In this paper we introduce a novel statistical transfer-function space which in the presence of noise, separates different materials in volume data sets. Our method adaptively estimates statistical properties, i.e. the mean value and the standard deviation, of the data values in the neighborhood of each sample point. These properties are used to define a transfer-function space which enables the distinction of different materials. Additionally, we present a novel approach for interacting with our new transfer-function space which enables the design of transfer functions based on statistical properties. Furthermore, we demonstrate that statistical information can be applied to enhance visual appearance in the rendering process. We compare the new method with 1D, 2D, and LH transfer functions to demonstrate its usefulness.

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@inproceedings{haidacher_2010_statTF,
  title =      "Volume Visualization based on Statistical Transfer-Function
               Spaces",
  author =     "Martin Haidacher and Daniel Patel and Stefan Bruckner and
               Armin Kanitsar and Meister Eduard Gr{\"o}ller",
  year =       "2010",
  abstract =   "It is a difficult task to design transfer functions for
               noisy data. In traditional transfer-function spaces, data
               values of different materials overlap. In this paper we
               introduce a novel statistical transfer-function space which
               in the presence of noise, separates different materials in
               volume data sets. Our method adaptively estimates
               statistical properties, i.e. the mean value and the standard
               deviation, of the data values in the neighborhood of each
               sample point. These properties are used to define a
               transfer-function space which enables the distinction of
               different materials. Additionally, we present a novel
               approach for interacting with our new transfer-function
               space which enables the design of transfer functions based
               on statistical properties. Furthermore, we demonstrate that
               statistical information can be applied to enhance visual
               appearance in the rendering process. We compare the new
               method with 1D, 2D, and LH transfer functions to demonstrate
               its usefulness.",
  pages =      "17--24",
  month =      mar,
  booktitle =  "Proceedings of the IEEE Pacific Visualization 2010",
  keywords =   "transfer function, statistics, shading, noisy data,
               classification",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2010/haidacher_2010_statTF/",
}