Poster
ThesisInformation
- Publication Type: Master Thesis
- Workgroup(s)/Project(s):
- Date: May 2015
- First Supervisor: Eduard Gröller
Abstract
Parameters and the process of setting them play a major role in the world of computer based visualisation, no matter whether it is a visualisation of information or of volume data. Finding suitable parameter values can take up most of the time in the visualisation process and users have to sensibly adjust a large number of parameters. Finding a useful parameter value distribution for achieving the desired visualisation result can be a cumbersome process which also depends on the user’s speed and experience. The purpose of this master’s thesis is to find a new and faster way to reach an appropriate parameter value distribution resulting in the desired visualisation.For this master’s thesis a prototype is developed which guides the user through a semi-automatic process of adjusting parameter values, which finally results in the desired visualisation of a scientific volume. Using this prototype enables the users to explore a large number of different parameter values within only a few iterations steps and a short amount of time. In order to do so we move away from the classic approach of setting parameters by adjusting sliders or combo boxes.
The idea of this thesis is to combine concepts that were already used in volume visualisation into a prototype. Our main strategy is to present pre-rendered images of the volume with different parameter values to the users. The images that are closest to the target visualisation can be selected and new images, similar to these, are shown. After some iterations of this process the users should have reached a visualisation that meets their expectations. The basis of our approach is a spreadsheet user interface.
Further we make use of the concept of high-level parameters, which are a combination of lowlevel parameters, like the specular exponent, to one single parameter, like contrast. The advantage of this concept is to have parameters which are more understandable to the users. We move away from the concept of displaying every single image in the spreadsheet interface, having multiple pages. Instead we use kMeans++ or DBScan with an automatic method to choose the distance parameter ? to cluster the images by similarity. This results in only the cluster centres, which are images, being presented to the user in the spreadsheet interface for exploration. Additionally, Locally Linear Embedding (LLE) is used to map single images into a global coordinate system. As a second new approach we use the distance between the images within the coordinate system as a similarity measure for kMeans++ and DBScan. To provide a fast calculation of the Locally Linear Embedding, which includes the nearest neighbours, the distance matrix and the Eigenvalues of the images, we use CUDA. The selection process consists of two different steps: exploration and refinement. Depending on the cluster size of the selected image, a re-clustering of the sub cluster is done if the user has reached the end of the cluster due to having explored all images and not achieving the desired final image. Thus a new set with varied parameter values is created and used to render new images. In contrast to the initially created set, the newly created one takes into account the explored parameter values from the images chosen by the user. This means that the range - in which the values of the single parameters are varied - is limited by the minimum and maximum value the parameter received during the before made exploration. Our tests showed that that by combining all these techniques it is possible to explore many different parameter values for high-level parameters in a very short time, and to achieve visualisations equal to those created by setting parameter values manually. In a short test our approach enabled two users, who are rather inexperienced in the field of volume visualisation, to create similar visualisations in fewer steps than by setting parameter values manually.
Additional Files and Images
Weblinks
No further information available.BibTeX
@mastersthesis{Hochmayr_Manuel_2015_PSE,
title = "Parameter Settings Exploration in Visualisation by Using a
Semi-automatic Process",
author = "Manuel Hochmayr",
year = "2015",
abstract = "Parameters and the process of setting them play a major role
in the world of computer based visualisation, no matter
whether it is a visualisation of information or of volume
data. Finding suitable parameter values can take up most of
the time in the visualisation process and users have to
sensibly adjust a large number of parameters. Finding a
useful parameter value distribution for achieving the
desired visualisation result can be a cumbersome process
which also depends on the user’s speed and experience. The
purpose of this master’s thesis is to find a new and
faster way to reach an appropriate parameter value
distribution resulting in the desired visualisation. For
this master’s thesis a prototype is developed which guides
the user through a semi-automatic process of adjusting
parameter values, which finally results in the desired
visualisation of a scientific volume. Using this prototype
enables the users to explore a large number of different
parameter values within only a few iterations steps and a
short amount of time. In order to do so we move away from
the classic approach of setting parameters by adjusting
sliders or combo boxes. The idea of this thesis is to
combine concepts that were already used in volume
visualisation into a prototype. Our main strategy is to
present pre-rendered images of the volume with different
parameter values to the users. The images that are closest
to the target visualisation can be selected and new images,
similar to these, are shown. After some iterations of this
process the users should have reached a visualisation that
meets their expectations. The basis of our approach is a
spreadsheet user interface. Further we make use of the
concept of high-level parameters, which are a combination of
lowlevel parameters, like the specular exponent, to one
single parameter, like contrast. The advantage of this
concept is to have parameters which are more understandable
to the users. We move away from the concept of displaying
every single image in the spreadsheet interface, having
multiple pages. Instead we use kMeans++ or DBScan with an
automatic method to choose the distance parameter ? to
cluster the images by similarity. This results in only the
cluster centres, which are images, being presented to the
user in the spreadsheet interface for exploration.
Additionally, Locally Linear Embedding (LLE) is used to map
single images into a global coordinate system. As a second
new approach we use the distance between the images within
the coordinate system as a similarity measure for kMeans++
and DBScan. To provide a fast calculation of the Locally
Linear Embedding, which includes the nearest neighbours, the
distance matrix and the Eigenvalues of the images, we use
CUDA. The selection process consists of two different steps:
exploration and refinement. Depending on the cluster size of
the selected image, a re-clustering of the sub cluster is
done if the user has reached the end of the cluster due to
having explored all images and not achieving the desired
final image. Thus a new set with varied parameter values is
created and used to render new images. In contrast to the
initially created set, the newly created one takes into
account the explored parameter values from the images chosen
by the user. This means that the range - in which the values
of the single parameters are varied - is limited by the
minimum and maximum value the parameter received during the
before made exploration. Our tests showed that that by
combining all these techniques it is possible to explore
many different parameter values for high-level parameters in
a very short time, and to achieve visualisations equal to
those created by setting parameter values manually. In a
short test our approach enabled two users, who are rather
inexperienced in the field of volume visualisation, to
create similar visualisations in fewer steps than by setting
parameter values manually.",
month = may,
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Institute of Computer Graphics and Algorithms, Vienna
University of Technology ",
URL = "https://www.cg.tuwien.ac.at/research/publications/2015/Hochmayr_Manuel_2015_PSE/",
}