Master's Thesis
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- Master's Thesis (46) (remove)
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- Institut für Computervisualistik (46) (remove)
Constituent parsing attempts to extract syntactic structure from a sentence. These parsing systems are helpful in many NLP applications such as grammar checking, question answering, and information extraction. This thesis work is about implementing a constituent parser for German language using neural networks. Over the past, recurrent neural networks have been used in building a parser and also many NLP applications. In this, self-attention neural network modules are used intensively to understand sentences effectively. With multilayered self-attention networks, constituent parsing achieves 93.68% F1 score. This is improved even further by using both character and word embeddings as a representation of the input. An F1 score of 94.10% was the best achieved by constituent parser using only the dataset provided. With the help of external datasets such as German Wikipedia, pre-trained ELMo models are used along with self-attention networks achieving 95.87% F1 score.
While Virtual Reality has been around for decades it gained new life in recent years. The release of the first consumer hardware devices allows fully immersive and affordable VR for the user at home. This availability lead to a new focus of research on technical problems as well as psychological effects. The concepts of presence, describing the feeling of being in the virtual place, body ownership and their impact are central topics in research for a long time and still not fully understood.
To enable further research in the area of Mixed Reality, we want to introduce a framework that integrates the users body and surroundings inside a visual coherent virtual environment. As one of two main aspects we want to merge real and virtual objects to a shared environment in a way such that they are no longer visually distinguishable. To achieve this the main focus is not supposed to be on a high graphical fidelity but on a simplified representation of reality. The essential question is, what level of visual realism is necessary to create a believable mixed reality environment that induces a sense of presence in the user? The second aspect considers the integration of virtual persons. Can characters be recorded and replayed in a way such that they are perceived as believable entities of the world and therefore act as a part of the users environment?
The purpose of this thesis was the development of a framework called Mixed Reality Embodiment Platform. This inital system implements fundamental functionalities to be used as a basis for future extensions to the framework. We also provide a first application that enables user studies to evaluate the framework and contribute to aforementioned research questions.
The goal of this thesis is to create and develop a concept for a mobile city guide combined with game-based contents.
The application is intented to support flexible and independent exploration of the city of Koblenz.
Based on the geographical data, historical information for and interesting stories of various places were provided in this application. These informations are combined with playful elements in order to create a motivating concept.
Therefore, related approaches were examined and, combined with own ideas, a new concept has been developed. This concept has been prototypically implemented as an Android application and afterwards evaluated by 15 test persons. A questionnaire was used to examine the operability, the motivation of game patterns and the additional value of the application.
Artificial neural networks is a popular field of research in artificial intelli-
gence. The increasing size and complexity of huge models entail certain
problems. The lack of transparency of the inner workings of a neural net-
work makes it difficult to choose efficient architectures for different tasks.
It proves to be challenging to solve these problems, and with a lack of in-
sightful representations of neural networks, this state of affairs becomes
entrenched. With these difficulties in mind a novel 3D visualization tech-
nique is introduced. Attributes for trained neural networks are estimated
by utilizing established methods from the area of neural network optimiza-
tion. Batch normalization is used with fine-tuning and feature extraction to
estimate the importance of different parts of the neural network. A combi-
nation of the importance values with various methods like edge bundling,
ray tracing, 3D impostor and a special transparency technique results in a
3D model representing a neural network. The validity of the extracted im-
portance estimations is demonstrated and the potential of the developed
visualization is explored.
A gonioreflectometer is a device to measure the reflection properties of arbitrary materials. In this work, such an apparatus is being built from easily obtainable parts. Therefore three stepper-motors and 809 light-emitting diodes are controlled by an Arduino microcontroller. RGB-images are captured with an industrial camera which serve as refelction data. Furthermore, a control software with several capture programs and a renderer for displaying the measured materials are implemented. These allow capturing and rendering entire bidirectional reflection distribution functions (BRDFs) by which also complex anisotropic material properties can be represented. Although the quality of the results has some artifacts due to shadows of the camera, these artifacts can be largely removed by using special algorithms like inpainting. In addition, the goniorefelctometer is applied to other use cases. One can perform 3D scans, light field capturing and light staging without altering the construction. The quality of these processes also meet the expectations in a positive way. Thus, the gonioreflectometer built in this work can be seen as a widely applicable and economical alternative to other publications.
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.
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.
Tractography on HARDI data
(2011)
Diffusion weighted imaging is an important modality in clinical imaging and the only possibility to gain insight into the human brain noninvasively and in-vivo. The applications of this imaging technique are diversified. It is used to study the brain, its structure, development and the functionality of the different areas. Further, important fields of application are neurosurgical planning, examinations of pathologies, investigation of Alzheimer-, strokes, and multiple sclerosis. This thesis gives a brief introduction to MRI and diffusion MRI. Based on this, the mostly used data representation in diffusion MRI in clinical imaging, the diffusion tensor, is introduced. As the diffusion tensor suffers from severe limitations new techniques subsumed under the term HARDI (high angular resolution diffusion imaging) are introduced and discussed in detail. Further, an extensive introduction to tractography, approaches that aim at reconstructing neuronal fibers, is given. Based on the knowledge fromthe theoretical part established tractography algorithms are redesigned to handle HARDI data and, thus, improve the reconstruction of neuronal fibers. Among these algorithms, a novel approach is presented that successfully reconstructs fibers on phantom data as well as on human brain data. Further, a novel global classification approach is presented to cluster voxels according to their diffusion properties.
Die Medizinische Visualisierung komplexer Gefäßbäume hat das Potential den klinischen Alltag in der Gefäßchirurgie zu erleichtern.
Dazu sind exakte, hochaufgelöste Darstellungen und echtzeitfähige Berechnungsmethoden notwendig. Bekannte Ansätze aus den Bereichen der direkten (z.B. Raycasting) und indirekten
(z.B. Marching Cubes) Volumenvisualisierung sind nicht in der Lage alle Anforderungen zufriedenstellend zu erfüllen. Verbesserte
Ergebnisse können mit hybriden Methoden erzielt werden, die unterschiedliche Visualisierungsverfahren kombinieren.
Im Rahmen dieser Arbeit wurde ein hybrides Renderingsystem zur Darstellung von Blutgefäßen entwickelt, das die Bildqualität durch Integration einer Marching Cubes Oberfläche in ein Raycasting–System optimiert, dabei Detailstrukturen erhält und ausreichende Performanz zur Interaktion bietet. Die Ergebnissezeigen die verbesserte Plastizität und Genauigkeit der Darstellung.Anhand von Experten– und Laienbefragungen konnte der Nutzen des Systems vor allem für die Patientenaufklärung nachgewiesen werden. Die Erschließung zusätzlicher Anwendungsgebiete ist durch die Weiterentwicklung des Renderers möglich.