Title: Importance-driven expressive visualization
Language: English
Authors: Viola, Ivan 
Qualification level: Doctoral
Advisor: Gröller, Eduard  
Assisting Advisor: Hansen, Charles 
Issue Date: 2005
Number of Pages: 107
Qualification level: Doctoral
In der vorliegenden Arbeit werden verschiede Verfahren zur expressiven Visualisierung von

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 (evels 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.
Keywords: Visualisierung; Volumendaten; Wichtigkeit; Daten; Klassifikation
URI: https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-19155
Library ID: AC04642123
Organisation: E186 - Institut für Computergraphik und Algorithmen 
Publication Type: Thesis
Appears in Collections:Thesis

Files in this item:

Show full item record

Page view(s)

checked on Jun 8, 2021


checked on Jun 8, 2021

Google ScholarTM


Items in reposiTUm are protected by copyright, with all rights reserved, unless otherwise indicated.