Master's Thesis
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- Master's Thesis (46) (remove)
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Institute
- Institut für Computervisualistik (46) (remove)
In scientific data visualization huge amounts of data are generated, which implies the task of analyzing these in an efficient way. This includes the reliable detection of important parts and a low expenditure of time and effort. This is especially important for the big-sized seismic volume datasets, that are required for the exploration of oil and gas deposits. Since the generated data is complex and a manual analysis is very time-intensive, a semi-automatic approach could on one hand reduce the time required for the analysis and on the other hand offer more flexibility, than a fully automatic approach.
This master's thesis introduces an algorithm, which is capable of locating regions of interest in seismic volume data automatically by detecting anomalies in local histograms. Furthermore the results are visualized and a variety of tools for the exploration and interpretation of the detected regions are developed. The approach is evaluated by experiments with synthetic data and in interviews with domain experts on the basis of real-world data. Conclusively further improvements to integrate the algorithm into the seismic interpretation workflow are suggested.
Statistical Shape Models (SSMs) are one of the most successful tools in 3Dimage analysis and especially medical image segmentation. By modeling the variability of a population of training shapes, the statistical information inherent in such data are used for automatic interpretation of new images. However, building a high-quality SSM requires manually generated ground truth data from clinical experts. Unfortunately, the acquisition of such data is a time-consuming, error-prone and subjective process. Due to this effort, the majority of SSMs is often based on a limited set of this ground truth training data, which makes the models less statistically meaningful. On the other hand, image data itself is abundant in clinics from daily routine. In this work, methods for automatically constructing a reliable SSM without the need of manual image interpretation from experts are proposed. Thus, the training data is assumed to be the result of any segmentation algorithm or may originate from other sources, e.g. non-expert manual delineations. Depending on the algorithm, the output segmentations will contain errors to a higher or lower degree. In order to account for these errors, areas of low probability of being a boundary should be excluded from the training of the SSM. Therefore, the probabilities are estimated with the help of image-based approaches. By including many shape variations, the corrupted parts can be statistically reconstructed. Two approaches for reconstruction are proposed - an Imputation method and Weighted Robust Principal Component Analysis (WRPCA). This allows the inclusion of many data sets from clinical routine, covering a lot more variations of shape examples. To assess the quality of the models, which are robust against erroneous training shapes, an evaluation compares the generalization and specificity ability to a model build from ground truth data. The results show, that especially WRPCA is a powerful tool to handle corrupted parts and yields to reasonable models, which have a higher quality than the initial segmentations.
One of the fundamental decisions during the development of any system is the representation of data. In computer graphics, objects are usually represented as sets of triangles. There are however many different variants with their own strengths and weaknesses. This thesis will explore distancefields as a representation for objects. Distancefields are functions, which map every point in space to the distance to the closest surface point. While this description is very simple, a number of interesting properties can be derived, allowing for a multitude of shapes, operations and effects. An overview of the necessary background and methods is given. Furthermore, some extended or new approaches are presented, such as displaying implicit surfaces, approximating indirect illumination or implementing a GPU tracer.
Object recognition is a well-investigated area in image-based computer vision and several methods have been developed. Approaches based on Implicit Shape Models have recently become popular for recognizing objects in 2D images, which separate objects into fundamental visual object parts and spatial relationships between the individual parts. This knowledge is then used to identify unknown object instances. However, since the emergence of aσordable depth cameras like Microsoft Kinect, recognizing unknown objects in 3D point clouds has become an increasingly important task. In the context of indoor robot vision, an algorithm is developed that extends existing methods based on Implicit Shape Model approaches to the task of 3D object recognition.
Point Rendering
(2021)
In this thesis different methods for rendering point data are shown and compared with each other. The methods can be divided into two categories. For one visual methods are introduced that strictly deal with the displaying of point primitves. The main problem here lies in the depiction of surfaces since point data, unlike traditional triangle meshes, doesn't contain any connectivity information. On the other hand data strucutres are shown that enable real-time rendering of large point clouds. Point clouds often contain large amounts of data since they are mostly generated through 3D scanning processes such as laser scanning and photogrammetry.
In this thesis, the performance of the IceCube projects photon propagation
code (clsim) is optimized. The process of GPU code analysis and perfor-
mance optimization is described in detail. When run on the same hard-
ware, the new version achieves a speedup of about 3x over the original
implementation. Comparing the unmodified code on hardware currently
used by IceCube (NVIDIA GTX 1080) against the optimized version run on
a recent GPU (NVIDIA A100) a speedup of about 9.23x is observed. All
changes made to the code are shown and their performance impact as well
as the implications for simulation accuracy are discussed individually.
The approach taken for optimization is then generalized into a recipe.
Programmers can use it as a guide, when approaching large and complex
GPU programs. In addition, the per warp job-queue, a design pattern used
for load balancing among threads in a CUDA thread block, is discussed in
detail.
Mit der Microsoft Kinect waren die ersten Aufnahmen von synchronisierten Farb- und Tiefendaten (RGB-D) möglich, ohne hohe finanzielle Mittel aufwenden zu müssen und neue Möglichkeiten der Forschung eröffneten sich. Mit fortschreitender Technik sind auch mobile Endgeräte in der Lage, immer mehr zu leisten. Lenovo und Asus bieten die ersten kommerziell erwerblichen Geräte mit RGB D-Wahrnehmung an. Mit integrierten Funktionen der Lokalisierung, Umgebungserkennung und Tiefenwahrnehmung durch die Plattform Tango von Google gibt es bereits die ersten Tests in verschiedenen Bereichen des Rechnersehens z.B. Mapping. In dieser Arbeit wird betrachtet, inwiefern sich ein Tango Gerät für die Objekterkennung eignet. Aus den Ausgangsdaten des Tango Geräts werden RGB D-Daten extrahiert und für die Objekterkennung verarbeitet. Es wird ein Überblick über den aktuellen Stand der Forschung und gewisse Grundlagen bezüglich der Tango Plattform gegeben. Dabei werden existierende Ansätze und Methoden für eine Objekterkennung auf mobilen Endgeräten untersucht. Die Implementation der Erkennung wird anhand einer selbst erstellten Datenbank von RGB-D Bildern gelernt und getestet. Neben der Vorstellung der Ergebnisse werden Verbesserungen und Erweiterungen für die Erkennung vorgeschlagen.
With the appearance of modern virtual reality (VR) headsets on the consumer market, there has been the biggest boom in the history of VR technology. Naturally, this was accompanied by an increasing focus on the problems of current VR hardware. Especially the control in VR has always been a complex topic.
One possible solution is the Leap Motion, a hand tracking device that was initially developed for desktop use, but with the last major software update it can be attached to standard VR headsets. This device allows very precise tracking of the user’s hands and fingers and their replication in the virtual world.
The aim of this work is to design virtual user interfaces that can be operated with the Leap Motion to provide a natural method of interaction between the user and the VR environment. After that, subject tests are performed to evaluate their performance and compare them to traditional VR controllers.
The Material Point Method (MPM) has proven to be a very capable simulation method in computer graphics that is able to model materials that were previously very challenging to animate [1, 2]. Apart from simulating singular materials, the simulation of multiple materials that interact with each other introduces new challenges. This is the focus of this thesis. It will be shown that the self-collision capabilities of the MPM can naturally handle multiple materials interacting in the same scene on a collision basis, even if the materials use distinct constitutive models. This is then extended by porous interaction of materials as in[3], which also integrates easily with MPM.It will furthermore be shown that regular single-grid MPM can be viewed as a subset of this multi-grid approach, meaning that its behavior can also be achieved if multiple grids are used. The porous interaction is generalized to arbitrary materials and freely changeable material interaction terms, yielding a flexible, user-controllable framework that is independent of specific constitutive models. The framework is implemented on the GPU in a straightforward and simple way and takes advantage of the rasterization pipeline to resolve write-conflicts, resulting in a portable implementation with wide hardware support, unlike other approaches such as [4].
In this thesis, we deal with the question if challenge, flow and fun in computer games are related to each other, and which influence the motivational, psychological components motivation of success, motivation of failure and the chance of success do have. In addition, we want to know if a free choice in the level of difficulty is the optimal way to flow. To examine these theories, a study based on an online survey was executed, in which the participants played the game “flOw“. The results were evaluated with the help of a two-factorial analysis of variance with repeated measurement and tests on correlation. Thereby we found out that there actually exists a relation between challenge, flow and fun and that motivation does matter indirectly.