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- Institut für Computervisualistik (30) (remove)
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.
Es wird ein Augmented-Reality Ansatz zur Erforschung modularer OSGi-Softwaresysteme präsentiert. Der Prototyp wird unter der Verwendung der Microsoft HoloLens implementiert. Module, wie Komponenten und Packages, werden in einer virtuellen Stadt dargestellt. Dieser Ansatz ermöglicht es dem Anwender, die Software-Architektur mittels intuitiver Navigation zu erkunden: Spracheingabe, Blickpunkt- und Gestenkontrolle. Eine multifunktionale Benutzeroberfläche wird vorgestellt, die für verschiedene Zielgruppen adaptiert werden kann. Viele veröffentlichte Visualisierungen weisen keine klare Zielgruppendefinition auf. Das Konzept kann leicht auf andere Darstellungsformen, wie beispielsweise der Inselmetapher übertragen werden. Erste Ergebnisse einer Evaluierung, die mittels kleiner strukturierter Interviews gewonnen werden konnten, werden präsentiert. Die Probanden mussten vier Programm-verständnis Aufgaben lösen und ihren Aufwand, sowie ihre Arbeitsbelastung einschätzen. Die Ergebnisse bilden eine gute Grundlage für weitere Forschung im Bereich der Software- Visualisierung in Augmented Reality.
Im Bereich Augmented Reality ist es von großer Bedeutung, dass virtuelle
Objekte möglichst realistisch in ein Kamerabild eingebettet werden. Nur
so ist es möglich, dem Nutzer eine immersive Erfahrung zu bieten. Dazu
gehört unter anderem, Verdeckung dieser Objekte korrekt zu behandeln.
Während schon verschiedene Ansätze existieren, dieses Verdeckungsproblem
zu beheben, wird in dieser Arbeit eine Lösung mittels Natural Image
Matting vorgestellt. Mit Hilfe einer Tiefenkamera wird das Kamerabild in
Vorder- und Hintergrund aufgeteilt und anschließend das virtuelle Objekt
im Bild platziert. Für Bereiche, in denen die Zugehörigkeit zu Vorder- oder
Hintergrund nicht eindeutig ist, wird anhand bekannter Pixel ein Transparenz-
Wert geschätzt. Es werden Methoden präsentiert, welche einen
Ablauf des Image Matting in Echtzeit ermöglichen. Zudem werden
Verbesserungsmöglichkeiten dieser Methoden präsentiert und gezeigt, dass
durch diese eine höhere Bildqualität für schwierige Szenen erreicht wird.
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.
One of the greatest goals in computer graphics is the aesthetic representation of objects. In addition to conventional methods, another field focuses on non-photorealistic renderings. The so-called example-based rendering is an area where users can transfer their art style to a pre-computed 3D rendering, using a hand-painted template. There are some algorithms that already provide impressive results, but their problem is that most of these procedures count as offline methods and are not able to produce results in real-time. For this reason, this work show a method that satisfies this condition. In addition, the influence of the run-time reduction on the results is investigated. Requirements are defined, to which the method and its results are examined. Other methods in this field are referenced and compared with their results.
The mitral valve is one of the four valves in the human heart. It is located in the left heart chamber and its function is to control the blood flow from the left atrium to the left ventricle. Pathologies can lead to malfunctions of the valve so that blood can flow back to the atrium. Patients with a faulty mitral valve function may suffer from fatigue and chest pain. The functionality can be surgically restored, which is often a long and exhaustive intervention. Thorough planning is necessary to ensure a safe and effective surgery. This can be supported by creating pre-operative segmentations of the mitral valve. A post-operative analysis can determine the success of an intervention. This work will combine existing and new ideas to propose a new approach to (semi-)automatically create such valve models. The manual part can guarantee a high quality model and reliability, whereas the automatic part contributes to saving valuable labour time.
The main contributions of the automatic algorithm are an estimated semantic separation of the two leaflets of the mitral valve and an optimization process that is capable of finding a coaptation-line and -area between the leaflets. The segmentation method can perform a fully automatic segmentation of the mitral leaflets if the annulus ring is already given. The intermediate steps of this process will be integrated into a manual segmentation method so a user can guide the whole procedure. The quality of the valve models generated by the method proposed in this work will be measured by comparing them to completely manually segmented models. This will show that commonly used methods to measure the quality of a segmentation are too general and do not suffice to reflect the real quality of a model. Consequently the work at hand will introduce a set of measurements that can qualify a mitral valve segmentation in more detail and with respect to anatomical landmarks. Besides the intra-operative support for a surgeon, a segmented mitral valve provides additional benefits. The ability to patient-specifically obtain and objectively describe the valve anatomy may be the base for future medical research in this field and automation allows to process large data sets with reduced expert dependency. Further, simulation methods that use the segmented models as input may predict the outcome of a surgery.
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.