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[Vio05]  Importance-Driven Expressive Visualization

Viola:2005:IDE (PhD thesis)
Author(s)Viola I.
Title« Importance-Driven Expressive Visualization »
SchoolTechnische Universität Wien
Year2005
AddressAustria
URLhttp://www.cg.tuwien.ac.at/research/publications/2005/phd-viola/

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.

BibTeX code
@phdthesis{Viola:2005:IDE,
  month = may,
  optwww = {},
  author = {Ivan Viola},
  optkey = {},
  optannote = {},
  opttype = {},
  url = {http://www.cg.tuwien.ac.at/research/publications/2005/phd-viola/},
  title = {{I}mportance-{D}riven {E}xpressive {V}isualization},
  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.},
  school = {Technische Universit{\"a}t Wien},
  address = {Austria},
  localfile = {papers/Viola.2005.IDE.pdf},
  year = {2005},
}

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