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2D Tensor Field Segmentation
(2009)
Many scientific applications deal with data from a multitude of different sources, e.g., measurements, imaging and simulations. Each source provides an additional perspective on the phenomenon of interest, but also comes with specific limitations, e.g. regarding accuracy, spatial and temporal availability. Effectively combining and analyzing such multimodal and partially incomplete data of limited accuracy in an integrated way is challenging. In this work, we outline an approach for an integrated analysis and visualization of the atmospheric impact of volcano eruptions. The data sets comprise observation and imaging data from satellites as well as results from numerical particle simulations. To analyze the clouds from the volcano eruption in the spatiotemporal domain we apply topological methods. Extremal structures reveal structures in the data that support clustering and comparison. We further discuss the robustness of those methods with respect to different properties of the data and different parameter setups. Finally we outline open challenges for the effective integrated visualization using topological methods.
This work is concerned with adaptive screen-space sampling for volume ray-casting. The goal is to reduce the
number of rays being cast into the scene and, thus, the overall number of sampling points. We guarantee reliable
images through explicit error control using an error estimator that is founded in the field of finite element methods
(FEM). FEM theory further provides a well-founded theory to prove the efficiency of the presented algorithm via
convergence analysis. We, therefore, compare the convergence behavior of our method against uniform subdivisions
and a refinement scheme that was presented in the context of CPU volume ray-casting. Minimizing
the number of sampling points is of interest for rendering large datasets where each evaluation might need an expensive
decompression. Furthermore, with increasing screen resolutions high-resolution images are created more
efficiently with our method.