Image Processing Reference
In-Depth Information
Visualization questions for these data types involve the selection of an appropriate
scale, or the consideration of the data through a proprietary (for example selective)
reconstruction of the data (based on certain scales of interest).
11.1.6 Other Types of Multifield Data
In addition to the major types of data already listed, a multifield framework can
be used as a representation for data such as tensor fields, where tensor components
are interpreted as individual fields, or time-dependent data, where the time-steps are
interpreted as individual fields.
Representing such data in a multifield form allows the use of existing visual-
ization methods such as coordinated multiple views with linking and brushing, or
focus+context visualization. Of course, an additional challenge is generated by the
fact that an important semantic aspect of the data (that the fields actually make up a
tensor or a time series) is possibly lost (or cannot be exploited).
11.1.7 Summary
If we look at the various types of multifield data, we see that nearly all of the types
require similar tasks to be performed, and in particular require the detection or visu-
alization of correlations between the fields. As a result, many of the techniques
applicable to one type will tend to be applicable to other types, and a categorization
by data type risks the repetitive discussion of the same techniques. We therefore con-
sider in the next section the techniques that are applicable to multifield visualization,
then return to the question of which approach to adopt.
11.2 Categorization by Visualization Approach
As we have seen above, one way to categorize multifield visualization is to focus on
the type of data. A second way to categorize is to observe that many techniques cut
across all of the types of data as discussed above. The advantage of this characteriza-
tion is that it gives a principled context in which to discuss not only those techniques
that have already been reported, but also in which to discuss classes of techniques
that could be introduced in the future. A second advantage of this approach is that we
can extrapolate more readily from techniques known to work for single-field data,
whether scalar, vector or tensor.
Broadly speaking, we can observe that the visualization of single-field data relies
on mapping the data to properties of the human visual system, on providing the user
interactive tools for isolating regions of the data, and on the detection of significant
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