Refine
Year of publication
Document Type
- Master's Thesis (16)
- Bachelor Thesis (12)
- Doctoral Thesis (9)
- Part of Periodical (7)
- Diploma Thesis (5)
- Study Thesis (2)
- Conference Proceedings (1)
Language
- English (52) (remove)
Keywords
- virtual reality (3)
- Bildverarbeitung (2)
- Computer Graphics (2)
- Computergraphik (2)
- Graphik (2)
- Line Space (2)
- OpenGL (2)
- Volumen-Rendering (2)
- tracking (2)
- Acceleration Structures (1)
- Action Recognition (1)
- Action Segmentation (1)
- Adobe Flex (1)
- Automatische Klassifikation (1)
- Avatar (1)
- Bildanalyse (1)
- Bildsegmentierung (1)
- Blickpunktabhängig (1)
- C++ (1)
- Casual Games (1)
- Coloskopie (1)
- Compute Shader (1)
- Computer Vision (1)
- Computer assisted communication (1)
- Computeranimation (1)
- Computertomografie (1)
- Computervisualistik (1)
- DTI (1)
- Darmpolyp (1)
- Data compression (1)
- Datenkompression (1)
- Deep Metric Learning (1)
- Diagnoseunterstützung (1)
- Diagnosis assistance (1)
- Diffusionsbildgebung (1)
- Digitale Bilder (1)
- ECSA (1)
- Entity Component System Architecture (1)
- Fabric Simulation (1)
- Facebook Application (1)
- Fiber Tracking (1)
- GPU (1)
- Gefäßanalyse (1)
- Gefühl (1)
- Gehirn (1)
- Grafikkarte (1)
- Grafikprogrammierung (1)
- Grails (1)
- Grails 1.2 (1)
- Graphicsprogramming (1)
- Graphik-Hardware (1)
- Human motion (1)
- IceCube (1)
- Image Processing (1)
- Image Understanding (1)
- Imitation Learning (1)
- Industrial-CT (1)
- Informatik (1)
- Inpainting-Verfahren (1)
- Konturfindung (1)
- Leichte Sprache (1)
- Linespace (1)
- Maschinelles Lernen (1)
- Material Point Method (1)
- MeVisLab (1)
- Merkmalsdetektion (1)
- Mitral Valve (1)
- Mitralklappe (1)
- Motion Capturing (1)
- Multidimensional (1)
- Multimodal Action Recognition (1)
- Multimodal Medical Image Analysis Cochlea Spine Non-rigid Registration Segmentation ITK VTK 3D Slicer CT MRI CBCT (1)
- Multiple Object Tracking (1)
- N-Body Simulation (1)
- N-Körper Simulation (1)
- Neutino (1)
- Objektentfernung (1)
- One-Shot Action Recognition (1)
- OpenGL Shading Language (1)
- Pattern Recognition (1)
- Pfadplanung (1)
- Physiksimulation (1)
- Programmierung (1)
- Random Finite Sets (1)
- Raytracing (1)
- Reflections (1)
- Reflektionen (1)
- Rendering (1)
- RoboCup (1)
- Robotik (1)
- Robust Principal Component Analysis (1)
- Sand (1)
- Schnee (1)
- Segmentation (1)
- Segmentierung (1)
- Shader (1)
- Social Games (1)
- Software Engineering (1)
- Specular (1)
- Statistical Shape Model (1)
- Stoffsimulation (1)
- Text (1)
- Texterkennung (1)
- Tracking-System (1)
- Transferfunction (1)
- Transferfunktion (1)
- Ultraschall (1)
- Ultrasound (1)
- Unterwasser-Pipeline (1)
- Unterwasserfahrzeug (1)
- Unterwasserkabel (1)
- VIACOBI (1)
- Vascular analysis (1)
- Virtual characters (1)
- Virtuelle Realität (1)
- Vocabulary Trainer (1)
- Volume Hatching (1)
- Wavelet (1)
- directed acyclic graphs (1)
- finite state automata (1)
- image processing (1)
- image warping (1)
- leap motion (1)
- machine learning (1)
- media competence model (1)
- multidimensional (1)
- natural language generation (1)
- path planning (1)
- performance optimization (1)
- plain language (1)
- privacy and personal data (1)
- privacy competence model (1)
- regular dag languages (1)
- risk (1)
- robotics (1)
- scaffolded writing (1)
- scene analysis (1)
- security awareness (1)
- stereoscopic rendering (1)
- volume rendering (1)
- warp divergence (1)
Institute
- Institut für Computervisualistik (52) (remove)
Since the invention of U-net architecture in 2015, convolutional networks based on its encoder-decoder approach significantly improved results in image analysis challenges. It has been proven that such architectures can also be successfully applied in different domains by winning numerous championships in recent years. Also, the transfer learning technique created an opportunity to push state-of-the-art benchmarks to a higher level. Using this approach is beneficial for the medical domain, as collecting datasets is generally a difficult and expensive process.
In this thesis, we address the task of semantic segmentation with Deep Learning and make three main contributions and release experimental results that have practical value for medical imaging.
First, we evaluate the performance of four neural network architectures on the dataset of the cervical spine MRI scans. Second, we use transfer learning from models trained on the Imagenet dataset and compare it to randomly initialized networks. Third, we evaluate models trained on the bias field corrected and raw MRI data. All code to reproduce results is publicly available online.
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].
Bio-medical data comes in various shapes and with different representations.
Domain experts use such data for analysis or diagnosis,
during research or clinical applications. As the opportunities to obtain
or to simulate bio-medical data become more complex and productive,
the experts face the problem of data overflow. Providing a
reduced, uncluttered representation of data, that maintains the data’s
features of interest falls into the area of Data Abstraction. Via abstraction,
undesired features are filtered out to give space - concerning the
cognitive and visual load of the viewer - to more interesting features,
which are therefore accentuated. To address this challenge, the dissertation
at hand will investigate methods that deal with Data Abstraction
in the fields of liver vasculature, molecular and cardiac visualization.
Advanced visualization techniques will be applied for this purpose.
This usually requires some pre-processing of the data, which will also
be covered by this work. Data Abstraction itself can be implemented
in various ways. The morphology of a surface may be maintained,
while abstracting its visual cues. Alternatively, the morphology may
be changed to a more comprehensive and tangible representation.
Further, spatial or temporal dimensions of a complex data set may
be projected to a lower space in order to facilitate processing of the
data. This thesis will tackle these challenges and therefore provide an
overview of Data Abstraction in the bio-medical field, and associated
challenges, opportunities and solutions.
Molecular dynamics (MD) as a field of molecular modelling has great potential to revolutionize our knowledge and understanding of complex macromolecular structures. Its field of application is huge, reaching from computational chemistry and biology over material sciences to computer-aided drug design. This thesis on one hand provides insights into the underlying physical concepts of molecular dynamics simulations and how they are applied in the MD algorithm, and also briefly illustrates different approaches, as for instance the molecular mechanics and molecular quantum mechanics approaches.
On the other hand an own all-atom MD algorithm is implemented utilizing and simplifying a version of the molecular mechanics based AMBER force field published by \big[\cite{cornell1995second}\big]. This simulation algorithm is then used to show by the example of oxytocin how individual energy terms of a force field function. As a result it has been observed, that applying the bond stretch forces alone caused the molecule to be compacted first in certain regions and then as a whole, and that with adding more energy terms the molecule got to move with increasing flexibility.
Tracking is an integral part of many modern applications, especially in areas like autonomous systems and Augmented Reality. For performing tracking there are a wide array of approaches. One that has become a subject of research just recently is the utilization of Neural Networks. In the scope of this master thesis an application will be developed which uses such a Neural Network for the tracking process. This also requires the creation of training data as well as the creation and training of a Neural Network. Subsequently the usage of Neural Networks for tracking will be analyzed and evaluated. This includes several aspects. The quality of the tracking for different degrees of freedom will be checked as well as the the impact of the Neural Network on the applications performance. Additionally the amount of required training data is investigated, the influence of the network architecture and the importance of providing depth data as part of the networks input. This should provide an insight into how relevant this approach could be for its adoption in future products.
This paper describes the robots TIAGo and Lisa used by
team homer@UniKoblenz of the University of Koblenz-Landau, Germany,
for the participation at the RoboCup@Home 2019 in Sydney,
Australia. We ended up first at RoboCup@Home 2019 in the Open Platform
League and won the competition in our league now three times
in a row (four times in total) which makes our team the most successful
in RoboCup@Home. We demonstrated approaches for learning from
demonstration, touch enforcing manipulation and autonomous semantic
exploration in the finals. A special focus is put on novel system components
and the open source contributions of our team. We have released
packages for object recognition, a robot face including speech synthesis,
mapping and navigation, speech recognition interface, gesture recognition
and imitation learning. The packages are available (and new packages
will be released) on http://homer.uni-koblenz.de.
The development of a game engine is considered a non-trivial problem. [3] The architecture of such simulation software must be able to manage large amounts of simulation objects in real-time while dealing with “crosscutting concerns” [3,p. 36] between subsystems. The use of object oriented paradigms to model simulation objects in class hierarchies has been reported as incompatible with constantly changing demands during game development [2, p. 9], resulting in anti-patterns and eventual, messy refactoring.[13]
Alternative architectures using data oriented paradigms revolving around object composition and aggregation have been proposed as a result. [13, 9, 1, 11]
This thesis describes the development of such an architecture with the explicit goals to be simple, inherently compatible with data oriented design, and to make reasoning about performance characteristics possible. Concepts are formally defined to help analyze the problem and evaluate results. A functional implementation of the architecture is presented together with use cases common to simulation software.
The mitral valve is one of four human heart valves. It is located in the left heart and acts as a unidirectional passageway for blood between the left atrium and the left ventricle. A correctly functioning mitral valve prevents a backflow of blood into the pulmonary circulation (lungs) and thus constitutes a vital part of the cardiac cycle. Pathologies of the mitral valve can manifest in a variety of symptoms with severity ranging from chest pain and fatigue to pulmonary edema (fluid accumulation in the tissue and air space of lungs), which may ultimately cause respiratory failure.
Malfunctioning mitral valves can be restored through complex surgical interventions, which greatly benefit from intensive planning and pre-operative analysis. Visualization techniques provide a possibility to enhance such preparation processes and can also facilitate post-operative evaluation. The work at hand extends current research in this field, building upon patient-specific mitral valve segmentations developed at the German Cancer Research Center, which result in triangulated 3D models of the valve surface. The core of this work will be the construction of a 2D-view of these models through global parameterization, a method that can be used to establish a bijective mapping between a planar parameter domain and a surface embedded in higher dimensions.
A flat representation of the mitral valve provides physicians with a view of the whole surface at once, similar to a map. This allows assessment of the valve's area and shape without the need for different viewing angles. Parts of the valve that are occluded by geometry in 3D become visible in 2D.
An additional contribution of this work will be the exploration of different visualizations of the 3D and 2D mitral valve representations. Features of the valve can be highlighted by associating them with specified colors, which can for instance directly convey pathology indicators.
Quality and effectiveness of the proposed methods were evaluated through a survey conducted at the Heidelberg University Hospital.
This work describes a novel software tool for visualizing anatomical segmentations of medical images. It was developed as part of a bachelor's thesis project, with a view to supporting research into automatic anatomical brain image segmentation. The tool builds on a widely-used visualization approach for 3D image volumes, where sections in orthogonal directions are rendered on screen as 2D images. It implements novel display modes that solve common problems with conventional viewer programs. In particular, it features a double-contour display mode to aid the user's spatial orientation in the image, as well as modes for comparing two competing segmentation labels pertaining to one and the same anatomical region. The tool was developed as an extension to an existing open-source software suite for medical image processing. The visualization modes are, however, suitable for implementation in the context of other viewer programs that follow a similar rendering approach.
The modified code can be found here: soundray.org/mm-segmentation-visualization.tar.gz.
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.