Master's Thesis - Studierstube Augmented Reality Project - Graz ...

2.1 Technical Visualization
• averaging of all volume elements on a ray produces an image similar to an X-
Ray-image,
• taking the first occurring value on a ray is called first hit and renders the first
pixels above a certain threshold. In the optimal case this technique would result
in the same as extracting a surface from the volume but the estimation of the
threshold can be time-consuming as well.
An one-dimensional transfer function may be applicable only to adjust the opacity
of the volume. Multidimensional functions can also be used to color certain parts of
the volume, despite the increasing complexity the design of the function may get accomplished
by trial and error. [He1996] identified a parameter optimization problem
and proposed user driven algorithms to optimize transfer functions. [Kniss2001] denoted
furthermore that the complexity of defining a transfer function is based in the
enormous number of degrees of freedom in which the user may get lost.
The automatic adjustment of adequate parameters is still a topic of research. The
currently best ways to define a multidimensional transfer function for arbitrary datasets
are mostly interactively driven as proposed by [Kniss2001], who defined manipulation
widgets for 3D transfer functions. They defined the axis of the 3D function space with
the data value, the gradient magnitude and the second derivative. To underline their
results they demonstrated their work for multivariate data in a case study [Kniss2002a].
Recently new and more powerful kinds of transfer function designs are developed.
[Bruckner2007] presented a technique for illustrative volume visualization to enhance
important features without rendering uninteresting ones. They introduced the concept
of style transfer functions by using the data values and eye-space normals, so thickness
controlled contours are possible by approximating the normal curvature along the
viewing direction.
In later chapters we concentrate on on one-dimensional transfer functions since
the focus of this work lies in the visualization of flow patterns, where direct volume
rendering approaches with opacity mapping serve for a spatial localization of these
patterns in the volume. In our opinion additionally mis-colored rendered morphological
data would lead to a confusion with the painted flow visualizations. Nevertheless,
these techniques will have to be kept in mind for a meaningful combination with flow
visualizations in future work.
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