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Semantic Depth of Field
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| Abstract:
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Pointing the user to the parts of a visual display that are
currently
the most relevant is an important task in information visualization.
The same problem exists in photography, where a number of solutions have
been known for a long time. One of these methods is depth of field (DOF),
which depicts objects in or out of focus depending on their distance
from the camera.
We propose the generalization of this idea to a concept we call
semantic
depth of field (SDOF), where the sharpness of objects is controlled by
their current relevance, rather than their distance. This enables the
user to quickly switch between different sets of criteria without having
to get used to a different visualization layout or geometric distortion.
We present several visual metaphors based on SDOF and show
examples of
their use. Because DOF is widely used in photography and cinematography,
SDOF should be easy to understand for most users.
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| Project:
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This project is a joint research activity between
1.) research on
information visualization
at the Institute of Software Technology
at Vienna University of Technology, Austria,
and
2.) the basic research on visualization
at VRVis,
Vienna, Austria.
Parts of this work have been carried out as part of
the Asgaard project which is funded
by the Austrian Science Fund
(FWF),
whereas other parts of this work have been carried out
as part of the basic research project ``interactive visualization''
in the VRVis research center,
which is funded by an Austrian
governmental research project called Kplus.
A paper with title ``Semantic Depth of Field''
(authored by
Robert Kosara [1],
Silvia Miksch [1], and
Helwig Hauser [2])
is available as a technical report
from VRVis.
Another web page about SDOF is available from the
Institute of Software Technology at Vienna University of Technology,
Austria.
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| Papers:
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More details about a user study, related to this work,
can be found in
VRVis technical report TR-VRVis-2001-028 (9 pages)
from December 2001.
A shorter version of this paper
is published in the Proceedings of the 4th Joint IEEE TCVG -
EUROGRAPHICS Symposium on Visualization
(VisSym 2002),
May 27-29, 2002, in Barcelona, Spain, pp. 205-210.
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| Video:
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(.avi[MPEG4]-file, ~1.4MB)
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This video gives an overview over the SDOF
technique
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Images
(to retrieve an enlarged version
of the images, click on them):
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Below (a, b, and c):
using SDOF in chess.
a) all pieces have a relevance r=1;
b) the white pieces covering a white knight (and the
knight itself) have r=1, all others r=0;
c) the black chessmen threatening a white knight (and
the white knight) have r=1, all others r=0
(from Garry Kasparov vs. Deep Blue, ``IBM Kasparov
vs. Deep Blue: The Rematch'', May 3, 1997, after the 23rd move).
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| a) The chessboard as it is known.
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| b) Which chessmen cover the white knight on e3?
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| c) Which chessmen threaten the white knight on e3?
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| Below: maps.
Setting the layer containing rivers to r=1,
and adjacent layers to smaller values according
to their distance makes the rivers stand out (left);
the same can be done with roads (right).
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Figures
from the paper:
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| Figure 1:
A portrait as an example for DOF in photography (left) - the blurred
objects surrounding the face are hardly noticeable.
An example for preattentive processing: the object different to all
others is immediately perceived (top right); text is another example
(bottom right).
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| Figure 3:
The basic idea of SDOF: applying DOF individually to
scene objects depending on their semantics.
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Figure 2:
Camera models. The pinhole camera model (top left) and the thin lens
model (top right). The focal length f is the distance from the lens
at which light rays parallel to the lens axis are focused (top right).
A smaller effective lens diameter D leads to a smaller
circles of confusion for out-of-focus points (bottom).
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| Figure 4:
Building blocks for SDOF: spatial arrangement of visualization,
relevance criterion, and camera model.
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| Figure 5:
Two functions are used to map objects to blur diameters. This
makes independent control of semantic and visualization parameters
possible.
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Figure 6:
Examples for different blur functions mapping relevance
values r to to blur factors b (for discussion, see the
paper).
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This page
is maintained by Helwig Hauser.
In case of questions, comments, etc., please
mailto:Hauser@VRVis.at.
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