Importance-Driven Expressive Visualization

Information

• Publication Type: Invited Talk
• Workgroup(s)/Project(s):
  • ExVisation: Expressive Visualization of Volumetric Data
• Date: 2005
• Event: Dissertation Thesis Report
• Location: University of Siegen, Germany
• Conference date: 11. November 2005 –

Abstract

In this talk several expressive visualization techniques for volumetric data are presented. The key idea is to classify the underlying data according to its prominence on the resulting visualization by importance value. The importance property drives the visualization pipeline to emphasize the most prominent features and to suppress the less relevant ones. The suppression can be realized globally, so the whole object is suppressed, or locally. A local modulation generates cut-away and ghosted views because the suppression of less relevant features occurs only on the part where the occlusion of more important features appears.

Features within the volumetric data are classified according to a new imension 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. An additional step to traditional volume rendering evaluates the areas of occlusion and assigns a particular level of sparseness. This step is denoted as importance compositing. Advanced schemes for importance compositing determine the resulting visibility of features and if the resulting visibility distribution does not correspond to the importance distribution different levels of sparseness are selected.

The applicability of importance-driven visualization is demonstrated on several examples from medical diagnostics scenarios, flow visualization, and interactive illustrative visualization.

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BibTeX

@talk{diss-thesis-siegen,
  title =      "Importance-Driven Expressive Visualization",
  author =     "Ivan Viola",
  year =       "2005",
  abstract =   "In this talk several expressive visualization techniques for
               volumetric data are presented. The key idea is to classify
               the underlying data according to its prominence on the
               resulting visualization by importance value. The importance
               property drives the visualization pipeline to emphasize the
               most prominent features and to suppress the less relevant
               ones. The suppression can be realized globally, so the whole
               object is suppressed, or locally. A local modulation
               generates cut-away and ghosted views because the suppression
               of less relevant features occurs only on the part where the
               occlusion of more important features appears.  Features
               within the volumetric data are classified according to a new
               imension 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. An additional step to
               traditional volume rendering evaluates the areas of
               occlusion and assigns a particular level of sparseness. This
               step is denoted as importance compositing. Advanced schemes
               for importance compositing determine the resulting
               visibility of features and if the resulting visibility
               distribution does not correspond to the importance
               distribution different levels of sparseness are selected. 
               The applicability of importance-driven visualization is
               demonstrated on several examples from medical diagnostics
               scenarios, flow visualization, and interactive illustrative
               visualization. ",
  event =      "Dissertation Thesis Report",
  location =   "University of Siegen, Germany",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2005/diss-thesis-siegen/",
}