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.

Research has shown that people recognize personality, gender, inner states and many other items of information by simply observing human motion. Therefore the expressive human motion seems to be a valuable non-verbal communication channel. On the quest for more believable characters in virtual three dimensional simulations a great amount of visual realism has been achieved during the last decades. However, while interacting with synthetic characters in real-time simulations, often human users still sense an unnatural stiffness. This disturbance in believability is generally caused by a lack of human behavior simulation. Expressive motions, which convey personality and emotional states can be of great help to create more plausible and life-like characters. This thesis explores the feasibility of an automatic generation of emotionally expressive animations from given neutral character motions. Such research is required since common animation methods, such as manual modeling or motion capturing techniques, are too costly to create all possible variations of motions needed for interactive character behavior. To investigate how emotions influence human motion relevant literature from various research fields has been viewed and certain motion rules and features have been extracted. These movement domains were validated in a motion analysis and implemented in a system in an exemplary manner capable of automating the expression of angry, sad and happy states in a virtual character through its body language. Finally, the results were evaluated in user test.

We present a non-linear camera pose estimator, which is able to handle a combined input of point and line feature correspondences. For three or more correspondences, the estimator works on any arbitrary number and choice of the feature type, which provides an estimation of the pose on a preferably small and flexible amount of 2D-3D correspondences. We also give an analysis of different minimization techniques, parametrizations of the pose data, and of error measurements between 2D and 3D data. These will be tested for the usage of point features, lines and the combination case. The result shows the most stable and fast working non-linear parameter set for pose estimation in model-based tracking.

Accurate snow simulation is key to capture snow's iconic visuals. Intricate methods exist that attempt to grasp snow behaviour in a holistic manner. Computational complexity prevents them from reaching real-time performance. This thesis presents three techniques making use of the GPU that focus on the deformation of a snow surface in real-time. The approaches are examined by their ability to scale with an increasing number of deformation actors and their visual portrayal of snow deformation. The findings indicate that the approaches maintain real-time performance well into several hundred individual deformation actors. However, these approaches each have their individual restrictions handicapping the visual results. An experimental approach is to combine the techniques at reduced deformation actor count to benefit from the detailed, merged deformation pattern.

In this thesis we present an approach to track a RGB-D camera in 6DOF andconstruct 3D maps. We first acquire, register and synchronize RGB and depth images. After preprocessing we extract FAST features and match them between two consecutive frames. By depth projection we regain the z-value for the inlier correspondences. Afterwards we estimate the camera motion by 3D point set alignment between the correspondence set using least-squares. This local motion estimate is incrementally applied to a global transformation. Additionally wernpresent methods to build maps based on point cloud data acquired by a RGB-D camera. For map creation we use the OctoMap framework and optionally create a colored point cloud map. The system is evaluated with the widespread RGB-D benchmark.