Visibility Histograms in Direct Volume Rendering

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: November 2008
• Diploma Examination: 4. November 2008
• First Supervisor:
  • Eduard Gröller
  • Stefan Bruckner

Abstract

This thesis introduces visibility histograms as a method for analyzing volumetric datasets. These histograms show how much the data points within a 3D dataset that have the same scalar value influence the image which is created by rendering the dataset with a particular transfer function and from a particular viewing direction. These histograms can be used to gain insights into the internal structure of volumetric datasets, in particular information about occlusions. Furthermore, the possibility of automatically calculating transfer functions which generate a particular visibility histogram when applied to a dataset from a particular viewing direction is explored. Two methods which can be used to calculate a matching transfer function for a visibility histogram are explained, one of which is based on a genetic algorithm approach, while the other is an heuristic.

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BibTeX

@mastersthesis{emsenhuber-2008-vhd,
  title =      "Visibility Histograms in Direct Volume Rendering",
  author =     "Gerlinde Emsenhuber",
  year =       "2008",
  abstract =   "This thesis introduces visibility histograms as a method for
               analyzing volumetric datasets. These histograms show how
               much the data points within a 3D dataset that have the same
               scalar value influence the image which is created by
               rendering the dataset with a particular transfer function
               and from a particular viewing direction. These histograms
               can be used to gain insights into the internal structure of
               volumetric datasets, in particular information about
               occlusions. Furthermore, the possibility of automatically
               calculating transfer functions which generate a particular
               visibility histogram when applied to a dataset from a
               particular viewing direction is explored. Two methods which
               can be used to calculate a matching transfer function for a
               visibility histogram are explained, one of which is based on
               a genetic algorithm approach, while the other is an
               heuristic.",
  month =      nov,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2008/emsenhuber-2008-vhd/",
}