TY - CHAP A1 - Kratz, Andrea A1 - Schöneich, Marc A1 - Zobel, Valentin A1 - Burgeth, Bernhard A1 - Scheuermann, Gerik A1 - Hotz, Ingrid A1 - Stommel, Markus T1 - Tensor Visualization Driven Mechanical Component Design T2 - Proc. IEEE Pacific Visualization Symposium 2014 Y1 - 2014 U6 - https://doi.org/10.1109/PacificVis.2014.51 SP - 145 EP - 152 ER - TY - CHAP A1 - Kuhn, Alexander A1 - Engelke, Wito A1 - Flatken, Markus A1 - Hege, Hans-Christian A1 - Hotz, Ingrid T1 - Topology-based Analysis for Multimodal Atmospheric Data of Volcano Eruptions T2 - Topological Methods in Data Analysis and Visualization IV Y1 - 2017 U6 - https://doi.org/10.1007/978-3-319-44684-4_2 SP - 35 EP - 50 PB - Springer CY - Cham, Schweiz ER - TY - GEN A1 - Kuhn, Alexander A1 - Engelke, Wito A1 - Flatken, Markus A1 - Hege, Hans-Christian A1 - Hotz, Ingrid T1 - Topology-based Analysis for Multimodal Atmospheric Data of Volcano Eruptions N2 - 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. T3 - ZIB-Report - 16-03 KW - multi-modal, intergrated data analysis, topology Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-57043 SN - 1438-0064 ER - TY - CHAP A1 - Reininghaus, Jan A1 - Günther, David A1 - Hotz, Ingrid A1 - Prohaska, Steffen A1 - Hege, Hans-Christian T1 - TADD: A Computational Framework for Data Analysis Using Discrete Morse Theory T2 - Mathematical Software - ICMS 2010 Y1 - 2010 U6 - https://doi.org/10.1007/978-3-642-15582-6_35 VL - 6327 SP - 198 EP - 208 PB - Springer ER - TY - GEN A1 - Reininghaus, Jan A1 - Günther, David A1 - Hotz, Ingrid A1 - Weinkauf, Tino A1 - Seidel, Hans Peter T1 - Combinatorial Gradient Fields for 2D Images with Empirically Convergent Separatrices Y1 - 2012 UR - http://arxiv.org/abs/1208.6523 ER - TY - GEN A1 - Reininghaus, Jan A1 - Hotz, Ingrid ED - Pascucci, Valerio ED - Tricoche, Xavier ED - Hagen, Hans ED - Tierny, Julien T1 - Combinatorial 2D Vector Field Topology Extraction and Simplification T2 - Topological Methods in Data Analysis and Visualization Y1 - 2011 SN - 978-3-642-15013-5 U6 - https://doi.org/10.1007/978-3-642-15014-2_9 SP - 103 EP - 114 PB - Springer ER - TY - GEN A1 - Reininghaus, Jan A1 - Hotz, Ingrid ED - Peikert, Ronald ED - Carr, Hamish ED - Hauser, Helwig T1 - Computational Discrete Morse Theory for Divergence-Free 2D Vector Fields T2 - Topological Methods in Data Analysis and Visualization. Theory, Algorithms, and Applications. (TopoInVis 2011) Y1 - 2012 SP - 3 EP - 14 PB - Springer ER - TY - GEN A1 - Reininghaus, Jan A1 - Kasten, Jens A1 - Weinkauf, Tino A1 - Hotz, Ingrid T1 - Combinatorial Feature Flow Fields: Tracking Critical Points in Discrete Scalar Fields N2 - We propose a combinatorial algorithm to track critical points of 2D time-dependent scalar fields. Existing tracking algorithms such as Feature Flow Fields apply numerical schemes utilizing derivatives of the data, which makes them prone to noise and involve a large number of computational parameters. In contrast, our method is robust against noise since it does not require derivatives, interpolation, and numerical integration. Furthermore, we propose an importance measure that combines the spatial persistence of a critical point with its temporal evolution. This leads to a time-aware feature hierarchy, which allows us to discriminate important from spurious features. Our method requires only a single, easy-to-tune computational parameter and is naturally formulated in an out-of-core fashion, which enables the analysis of large data sets. We apply our method to a number of data sets and compare it to the stabilized continuous Feature Flow Field tracking algorithm. T3 - ZIB-Report - 11-02 KW - data analysis KW - feature detection KW - time-varying data KW - topology-based techniques Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-12151 ER - TY - JOUR A1 - Reininghaus, Jan A1 - Kasten, Jens A1 - Weinkauf, Tino A1 - Hotz, Ingrid T1 - Efficient Computation of Combinatorial Feature Flow Fields JF - Transactions on Visualization and Computer Graphics Y1 - 2012 U6 - https://doi.org/10.1109/TVCG.2011.269 VL - 18 IS - 9 SP - 1563 EP - 1573 ER - TY - JOUR A1 - Reininghaus, Jan A1 - Kotava, N. A1 - Günther, David A1 - Kasten, Jens A1 - Hagen, Hans A1 - Hotz, Ingrid T1 - A Scale Space Based Persistence Measure for Critical Points in 2D Scalar Fields JF - Visualization and Computer Graphics, IEEE Transactions on Y1 - 2011 U6 - https://doi.org/10.1109/TVCG.2011.159 VL - 17 IS - 12 SP - 2045 EP - 2052 ER -