Feature-Based Visualization of Multifields - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 177

Chapter 17
Feature-Based Visualization of Multifields

Harald Obermaier and Ronald Peikert

Abstract Feature-based techniques are one of the main categories of methods used
in scientific visualization. Features are structures in a dataset that are meaningful
within the scientific or engineering context of the dataset. Extracted features can
be visualized directly, or they can be used indirectly for modifying another type
of visualization. In multifield data, each of the component fields can be searched
for features, but in addition, there can be features of the multifield which rely on
information form several of its components and which cannot be found by searching
in a single field. In this chapter we give a survey of feature-based visualization of
multifields, taking both of these feature types into account.

17.1 Feature Extraction in Scientific Visualization

Scientific visualization has adopted the concept of a feature fromcomputer vision [ 6 ],
where it describes a salient structure of an image. Some of the most common image
features are edges, ridges, corners, and blobs. Features are important in various
applications, such as object recognition and tracking.
In visualization, features are used to put a focus on those parts that are of interest
in the context of a certain research or engineering problem. For example, in flow
data sets, features can be shock waves, vortices, recirculation, boundary layers, and
separation and attachment lines. By restricting the visualization of a dataset to its
features, its visual complexity can be substantially reduced. A visualization can
consist of only the extracted features, together with some context information, but

( B )
UC Davis, Davis, USA
e-mail: [e-mail]
R. Peikert
ETH Zurich, Zurich, Switzerland
e-mail: [e-mail]

H. Obermaier