Image Processing Reference
In-Depth Information
Table 13.1
Table illustrating
a classification of
multi-variate glyph-based
visualization techniques
based on the visualization
dimensionality and the visual
channels required to depict
thedataset
Visual channel
Visualization dimensionality
2D
2.5D 3D
Color
[
5
]
[
3
]
[
21
]
[
11
] [
6
]
[
12
]
[
22
] [
16
]
[
9
]
[
2
]
[
10
]
[
15
]
[
8
]
Shape
[
8
]
[
1
]
[
13
]
[
21
]
[
9
]
[
7
]
[
10
]
[
15
]
Size
[
25
] [
3
]
[
21
]
[
22
] [
16
]
[
9
]
[
20
]
[
2
]
[
15
]
Texture
[
22
] [
3
]
[
6
]
Opacity
[
11
]
[
15
]
[
22
]
mapping each data attribute. We further cluster the techniques with respect to the
spatial dimensionality of the visualization e.g., 2D, 2.5D and 3D. Texture can be
subjective in terms of glyph-based classification, however, we find that it is very
relevant in the research of multi-field. The following work can be acknowledged
without the use of this classification, but we include this in the table for completeness.
13.2.1 Spatial Dimensionality: 2D
A common technique for representing multi-field data is to overlay multiple visual-
izations onto a single image. Kirby et al. [
11
] stochastically arrange multiple visu-
alization layers to minimize overlap. Given a permutation of layers, a user-specified
importance value is attached to each visualization of increasing weights in order
to provide greater emphasis to higher layers. Visual cues such as color and opacity
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