Importance-Driven Feature Enhancement in Volume Visualization

Ivan Viola, Armin Kanitsar, Meister Eduard Gröller
Importance-Driven Feature Enhancement in Volume Visualization
IEEE Transactions on Visualization and Computer Graphics, 11(4):408-418, 2005. [paper]

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Abstract

This paper presents importance-driven feature enhancement as a technique for the automatic generation of cut-away and ghosted views out of volumetric data. The presented focus+context approach removes or suppresses less important parts of a scene to reveal more important underlying information. however, less important parts are fully visible in those regions, where important visual information is not lost, i.e., more relevant features are not occluded. Features within the volumetric data are first classified according to a new dimension denoted as object importance. This property determines which structures should be readily discernible and which structures are less important. Next, for each feature various representations (levels of sparseness) from a dense to a sparse depiction are defined. Levels of sparseness define a spectrum of optical properties or rendering styles. The resulting image is generated by ray-casting and combining the intersected features proportional to their importance (importance compositing). The paper includes an extended discussion on several possible schemes for levels of sparseness specification. Furthermore different approaches to importance compositing are treated.

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BibTeX

@article{viola-2005-imp,
  title =      "Importance-Driven Feature Enhancement in Volume
               Visualization",
  author =     "Ivan Viola and Armin Kanitsar and Meister Eduard Gr\"{o}ller",
  year =       "2005",
  abstract =   "This paper presents importance-driven feature enhancement as
               a technique for the automatic generation of cut-away and
               ghosted views out of volumetric data. The presented
               focus+context approach removes or suppresses less important
               parts of a scene to reveal more important underlying
               information. however, less important parts are fully visible
               in those regions, where important visual information is not
               lost, i.e., more relevant features are not occluded.
               Features within the volumetric data are first classified
               according to a new dimension denoted as object importance.
               This property determines which structures should be readily
               discernible and which structures are less important. Next,
               for each feature various representations (levels of
               sparseness) from a dense to a sparse depiction are defined.
               Levels of sparseness define a spectrum of optical properties
               or rendering styles. The resulting image is generated by
               ray-casting and combining the intersected features
               proportional to their importance (importance compositing).
               The paper includes an extended discussion on several
               possible schemes for levels of sparseness specification.
               Furthermore different approaches to importance compositing
               are treated.",
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  number =     "4",
  volume =     "11",
  pages =      "408--418",
  keywords =   "non-photorealistic techniques, view-dependent visualization,
               volume rendering, focus+context techniques, level-of-detail
               techniques",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2005/viola-2005-imp/",
}