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Die vorliegende Arbeit beschäftigt sich mit der Betrachtung der Rolle von Vertrauen zwischen den Kapitalnehmern und Kapitalgebern auf einer der größten Crowdfunding-Plattformen, Kickstarter. Kernthema ist die Beantwortung der Forschungsfragen, wie das Vertrauen zwischen den Kapitalgebern und Kapitalnehmern im Kontext von Crowdfunding entsteht, welche Faktoren das Maß von Vertrauen in dieser Beziehung beeinflussen und welche Signale für die Vertrauensbildung verantwortlich sind. Das Ziel dieser Arbeit besteht darin, das von Zerwas, Kilian und von Kortzfleisch 2015 vorgestellte konzeptuelle Modell der Einflussfaktoren von Vertrauen im Kontext von Crowdfunding aus Sicht der Kapitalgeber zu überprüfen und gegebenenfalls zu erweitern.
Auf Grundlage einer Literaturrecherche und qualitativer, leitfadenorientierter Interviews werden die gesammelten Daten diskutiert, bestehende Faktoren verifiziert und weiterführend neue Faktoren und Signale identifiziert, die die Bildung von Vertrauen auf Crowdfunding-Plattformen beeinflussen.
Als Ergebnis werden die Überarbeitung sowie Erweiterung des Ausgangsmodells um die neuen Faktoren geographische Nähe, Vermittlerqualität und Verständnis vom Konzept Crowdfunding vorgeschlagen. Anhand der Häufigkeit der Erwähnung und Relevanz der Faktoren und beeinflussenden Signale in den durchgeführten Interviews wird weiterführend eine Gewichtung der Einzelfaktoren vorgenommen. Abschließend werden Implikationen und Bedeutung für Wissenschaft und Forschung diskutiert.
This work covers techniques for interactive and physically - based rendering of hair for computer generated imagery (CGI). To this end techniques
for the simulation and approximation of the interaction of light with hair are derived and presented. Furthermore it is described how hair, despite such computationally expensive algorithms, can be rendered interactively.
Techniques for computing the shadowing in hair as well as approaches to render hair as transparent geometry are also presented. A main focus of
this work is the DBK-Buffer, which was conceived, implemented and evaluated. Using the DBK-Buffer, it is possible to render thousands of hairs as
transparent geometry without being dependent on either the newest GPU hardware generation or a great amount of video memory. Moreover, a comprehensive evaluation of all the techniques described was conducted with respect to the visual quality, performance and memory requirements. This
revealed that hair can be rendered physically - based at interactive or even at real - time frame rates.
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.