Volume Visualization based on Statistical Transfer-Function Spaces

Martin Haidacher, Daniel Patel, Stefan Bruckner, Armin Kanitsar, Meister Eduard Gröller

INPROCEEDINGS, Proceedings of the IEEE Pacific Visualization Symposium 2010, March, 2010

• www

Abstract

It is a difficult task to design transfer functions for noisy data. In traditional transfer-function spaces, data values of different materials overlap. In this paper we introduce a novel statistical transfer-function space which in the presence of noise, separates different materials in volume data sets. Our method adaptively estimates statistical properties, i.e. the mean value and the standard deviation, of the data values in the neighborhood of each sample point. These properties are used to define a transfer-function space which enables the distinction of different materials. Additionally, we present a novel approach for interacting with our new transfer-function space which enables the design of transfer functions based on statistical properties. Furthermore, we demonstrate that statistical information can be applied to enhance visual appearance in the rendering process. We compare the new method with 1D, 2D, and LH transfer functions to demonstrate its usefulness.

Published

Proceedings of the IEEE Pacific Visualization Symposium 2010

• Pages: 17–24
• Location: Taipei, Taiwan
• Date: March 2010
• URL: http://www.cg.tuwien.ac.at/research/publications/2010/haidacher_2010_statTF/

Media

BibTeX

@inproceedings{haidacher10statisticalTF,
  title = "Volume Visualization based on Statistical Transfer-Function Spaces",
  author = "Martin Haidacher and Daniel Patel and Stefan Bruckner and Armin Kanitsar and Meister Eduard Gr{\"o}ller",
  year = "2010",
  booktitle = {Proceedings of the IEEE Pacific Visualization Symposium 2010},
  abstract = "It is a difficult task to design transfer functions for noisy data. 
In traditional transfer-function spaces, data values of different materials overlap. 
In this paper we introduce a novel statistical transfer-function space which
in the presence of noise, separates different materials in volume data sets. 
Our method adaptively estimates statistical properties, i.e. the mean value and 
the standard deviation, of the data values in the neighborhood of each sample 
point. These properties are used to define a transfer-function space which 
enables the distinction of different materials. Additionally, we present a novel
approach for interacting with our new transfer-function space which enables the 
design of transfer functions based on statistical properties. Furthermore, we 
demonstrate that statistical information can be applied to enhance visual 
appearance in the rendering process. We compare the new method with 1D, 2D, and 
LH transfer functions to demonstrate its usefulness.",
  pages = {17--24},
  month = {March},
  location = {Taipei, Taiwan},


  URL = {http://www.cg.tuwien.ac.at/research/publications/2010/haidacher_2010_statTF/},
}