Image Processing Reference
In-Depth Information
effects of, or making a comparison between, different fields. In this section, we first
consider different visual channels that have been used in visualization (Sect.
12.2
).
We then discuss various constructive operations on visual channels, showing various
examples of channel fusions (Sect.
12.3
). This is followed by an examination of a
special situation where multiple fields are a set of fields from different temporal steps
(Sect.
12.4
). Finally, we consider a collection of alternatives, in which data mapping
plays a critical role in reducing the complexity of visual mapping (Sect.
12.5
).
The fusion of visual channels is highly sensitive to the limitations of human per-
ception as well as application-specific visual metaphors. In scientific visualization, it
is often constrained by elementary properties of the data. For instance, when geom-
etry (e.g., an isosurface) is an intrinsic property of one of the fields, the flexibility in
using geometry to depict other types of properties in a composite visualization is sig-
nificantly reduced.When color is an intrinsic property of one of the fields (e.g., visible
spectrum in remote sensing data), it is usually very difficult to fuse different color
metaphors by introducing virtual colors for other types of properties while maintain-
ing the original color representation. Furthermore, the number of visual channels is
limited; hence their use cannot be scaled to an arbitrary number of multiple fields.
Because of these reasons, it is important to avoid overloading of the visual channels
by enabling users to choose a subset of multiple fields or a subset of properties to
be visualized (see Sect.
12.5
), and by employing appropriate analytic methods for
filtering out unimportant data and selecting features to be highlighted (see Chaps.
17
12.2 Visual Channels in Multifield Visualization
There are many types of visual channels that can be utilised in visualization. Bertin
provided a comprehensive study on several visual channels typically used in geo-
spatial visualization in general and cartography in particular [
1
]. However, mul-
tifield visualization exhibits many characteristics that are untypical in geo-spatial
visualization. For example, most multifield datasets represent objects or phenom-
ena in a continuous 3D spatial domain, and many encode directional and temporal
information at a much larger scale than ordinary geographical datasets. Hence it
is not uncommon for multifield visualization to make use of more visual channels
than those commonly-available in geo-spatial visualization. Visual channels can be
roughly divided into the classes of
Geometric
,
Optical
,
Relational
, and
Semantic
channels [
7
]. This classification focuses on the
effect
of a visual channel rather than
its
cause
. For example, the
curvature
of a surface is indubitably a geometric property,
but one of the most effective ways to depict this property (i.e., to cause this effect) is
shading
, which is an optical channel. On the other hand, geometric channels can also
be used to influence optical perception, hence contributing to the formation of optical
channels. For example,
textures
, which are made of different geometric components,
are commonly used to convey different scales of
brightness
that is an optical channel.
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