In der vorliegenden Arbeit werden verschiede Verfahren zur expressiven Visualisierung von
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dc.description.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.<br />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 (evels of sparseness) from a dense to a sparse depiction are defined. Levels of sparseness define a spectrum of optical properties or rendering styles.<br />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.<br />The applicability of importance-driven visualization is demonstrated on several examples from medical diagnostics scenarios, flow visualization, and interactive illustrative visualization.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Visualisierung
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dc.subject
Volumendaten
de
dc.subject
Wichtigkeit
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dc.subject
Daten
de
dc.subject
Klassifikation
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dc.title
Importance-driven expressive visualization
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dc.type
Thesis
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dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Ivan Viola
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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dc.contributor.assistant
Hansen, Charles
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tuw.publication.orgunit
E186 - Institut für Computergraphik und Algorithmen
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC04642123
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dc.description.numberOfPages
107
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-19155
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0003-4248-6574
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dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.orcid
0000-0002-8569-4149
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item.languageiso639-1
en
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item.fulltext
with Fulltext
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item.openaccessfulltext
Open Access
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item.mimetype
application/pdf
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item.openairetype
doctoral thesis
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item.grantfulltext
open
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.cerifentitytype
Publications
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crisitem.author.dept
E193-02 - Forschungsbereich Computer Graphics
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crisitem.author.parentorg
E193 - Institut für Visual Computing and Human-Centered Technology