The Haunted Swamps of Heuristics: Uncertainty in Problem Solving - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 54

In the following recent examples of parameter-space exploration in the visualiza-
tion domain are shortly discussed.
Ma [ 12 ] introduced a visualization system which presents information on how
parameter changes affect the result image as an image graph based on data gener-
ated during an interactive exploration process. Berger et al. [ 4 ] study continuous
parameter spaces in order to guide the user to regions of interest. Their system uses
continuous 2D scatter plots and parallel coordinates for a continuous analysis of a
discretely sampled parameter space. Not sampled areas of the parameter space are
filled with predictions. The uncertainty of the predictions is taken into account and
also visualized. The stability of the results with respect to the input parameters is
visualized and explored.
In the work by Amirkhanov et al. [ 1 ] parameter space exploration is carried out
in order to detect the optimal specimen placement on a rotary plate for industrial 3D
X-ray computed tomography. The parameter space is represented by Euler angles
defining the orientation of the specimen. The parameter settings providing the optimal
scanning result were determined using a visual analysis tool. The stability of the result
with respect to these parameters was additionally taken into account.
Analyzing how segmentation performs when parameters change and finding the
optimal set of parameters is a tedious and time-consuming task. It is usually done
manually by the developers of segmentation algorithms. Torsney-Weir et al. [ 16 ]
presented a system to simplify this task by providing an interactive visualization
framework. The Tuner tool samples the parameter space of a segmentation algo-
rithm and runs computations off-line. A statistical model is then applied for the
segmentation response. Hyper slices [ 19 ] of the parameter space and 2D scatter plots
are used to visualize these data. Based on the prediction model, additional samples of
parameter space may be specified in the regions of interest. The tool allows finding
the optimal parameter values and estimating the segmentation algorithm's robustness
with respect to its parameters.
FluidExplorer by Bruckner and Möller [ 7 ] is an example of goal-driven parameter
exploration. They explore the parameters of physically-based simulations for the
generation of visual effects such as smoke or explosions. First, the set of simulation
runs with various parameter sets is run off-line. Then sampling and spatio-temporal
clustering techniques are utilized to generate an overview of the achievable results.
Temporal evolution of various simulation clusters is shown. The goal is to find the
set of parameters resulting in a certain visual appearance. The metric is defined via
user interaction when the user explores the simulation space.
The work of Waser et al. [ 17 ]uses World Lines to study complex physical sim-
ulations. In such time-dependent simulations parameters can change their values at
arbitrary moments in time. Decision support is provided by the ability to explore
alternative scenarios. A World Line is introduced as a visual combination of user
events and their effects in order to present a possible future. The proposed setup
enables users to interfere and add new information quickly to find the most appro-
priate simulation outcome. The usefulness of the technique is shown on a flood-
ing scenario where a smoothed particle hydrodynamics simulation is used. Waser
et al. further expand their framework in [ 18 ]. The authors take uncertainty of the