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Volume Visualization based on Statistical Transfer-Function Spaces

Martin Haidacher, Daniel Patel, Stefan Bruckner, Armin Kanitsar, M. Eduard Gröller

CONFERENCE PAPER: In Proceedings of IEEE Pacific Visualization 2010, pp. 17–24, 2010. DOI: 10.1109/PACIFICVIS.2010.5429615

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 IEEE Pacific Visualization 2010

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BibTeX

@INPROCEEDINGS{Haidacher-2010-VVS,
  author = {Martin Haidacher and Daniel Patel and Stefan Bruckner and Armin Kanitsar
	and M. Eduard Gr{\"o}ller},
  title = {Volume Visualization based on Statistical Transfer-Function Spaces},
  booktitle = {Proceedings of IEEE Pacific Visualization 2010},
  year = {2010},
  pages = {17--24},
  month = mar,
  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.},
  doi = {10.1109/PACIFICVIS.2010.5429615},
  keywords = {transfer function, statistics, shading, noisy data, classification},
  url = {http://www.cg.tuwien.ac.at/research/publications/2010/haidacher_2010_statTF/}
}






 Last Modified: Stefan Bruckner, 2014-08-15