Categorization - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 110

multifield to a single field, which is then visualized directly using existing techniques.
Thesemethodsmay includemeasures of complexity or correlation between variables,
or derived properties such as vorticity.
While these methods can be very effective, they tend to work best at detecting
relationships that are known or suspected. This is because the choice of the summa-
tive property is usually guided by a sense that a particular aspect of the data set is
significant. Moreover, they presume that the phenomenon being studied is uniform
throughout the domain: thus, if two properties are weakly correlated in one area, but
strongly correlated elsewhere, these methods will be less successful.
11.2.3 Interactive Exploration
A third category of visualization techniques relies on the experience and intuition
of the user, by providing an interactive tool for exploring the data. Inevitably, this
relies on visual channel mapping and derived fields to give the user sufficient insight
to identify features, and increasingly, on feature detection as well.
Interactive exploration can operate bymanipulation of the visual channelmapping,
by the provision of geometric tools to identify regions of the data, by selection
of paradigm points or regions as seeds for similarity measures, by combination of
properties through logical rules, or by reference to abstract descriptions or secondary
visualizations.
While often the most effective approach, interactive exploration starts breaking
down with larger data sets, as does direct visualization itself, as the volume of data
outstrips the humans visual and cognitive capacity to understand the data.
11.2.4 Feature Detection and Analysis
All three categories described so far share a common difficulty: that, as the amount of
data increases, less and less of it can be presented to the user. In short, the question is
not “how can we visualize the data”, but “what subset of the data can we visualize”.
As a result, visualization techniques have increasingly relied on abstract definitions
of features, either specific to a domain, specific to a type of data, or common to
multiple domains and data types. These features are detected computationally and
presented to the user either as the answer to a question, or as the seeds to an interactive
exploration.
Philosophically, these methods shade off into the disciplines of image analysis,
computer vision and data analysis, all of which share a common interest in detecting
features in masses of data. However, one set of methods which is distinctive in visu-
alization is the reliance on formal mathematics such as topology to extract abstract
features either for further analysis or for direct visualization.