Importance-driven expressive visualization / Ivan Viola
VerfasserViola, Ivan
Begutachter / BegutachterinGröller, Eduard ; Hansen, Charles
UmfangVI, 107 S. : Ill., graph. Darst.
HochschulschriftWien, Techn. Univ., Diss., 2005
Zsfassung in dt. Sprache
Bibl. ReferenzOeBB
Schlagwörter (GND)Visualisierung / Volumendaten / Wichtigkeit / Daten / Klassifikation
URNurn:nbn:at:at-ubtuw:1-19155 Persistent Identifier (URN)
 Das Werk ist frei verfügbar
Importance-driven expressive visualization [9.2 mb]
Zusammenfassung (Deutsch)

In der vorliegenden Arbeit werden verschiede Verfahren zur expressiven Visualisierung von

Zusammenfassung (Englisch)

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.