Importance-Driven Expressive Visualization

Information

• Publication Type: PhD-Thesis
• Workgroup(s)/Project(s):
  • ADAPT: Advanced Diagnosis, Analysis and Planning Tools (in Medicine)
• Date: June 2005
• Date (Start): May 2002
• Date (End): June 2005
• First Supervisor: Eduard Gröller

Abstract

In this thesis 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 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. 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

@phdthesis{phd-viola,
  title =      "Importance-Driven Expressive Visualization",
  author =     "Ivan Viola",
  year =       "2005",
  abstract =   "In this thesis 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 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. 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.",
  month =      jun,
  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/2005/phd-viola/",
}