Transfer Function design toolbox for full-color volume datasets

In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromat...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE Pacific Visualization Symposium S. 171 - 179
Hauptverfasser: Falk, Martin, Hotz, Ingrid, Ljung, Patric, Treanor, Darren, Ynnerman, Anders, Lundstrom, Claes
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2017
Schlagworte:
Color
Computer Graphics [I.3.3]: Picture/Image Generation-Display algorithms
Computer Graphics [I.3.6]: Methodology and Techniques
Computer Graphics [I.3.7]: Three-Dimensional Graphics and Realism
Computer Graphics [I.3.8]: Applications
Data visualization
Histograms
Image color analysis
Rendering (computer graphics)
Three-dimensional displays
Transfer functions
ISSN:2165-8773
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YC B C R are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.
ISSN:2165-8773
DOI:10.1109/PACIFICVIS.2017.8031591