TY - CHAP A1 - Gossing, Anne A1 - Beckert, Andreas A1 - Fischer, Christoph A1 - Klenert, Nicolas A1 - Natarajan, Vijay A1 - Pacey, George A1 - Vogt, Thorwin A1 - Rautenhaus, Marc A1 - Baum, Daniel T1 - A Ridge-based Approach for Extraction and Visualization of 3D Atmospheric Fronts T2 - 2024 IEEE Visualization and Visual Analytics (VIS) N2 - An atmospheric front is an imaginary surface that separates two distinct air masses and is commonly defined as the warm-air side of a frontal zone with high gradients of atmospheric temperature and humidity. These fronts are a widely used conceptual model in meteorology, which are often encountered in the literature as two-dimensional (2D) front lines on surface analysis charts. This paper presents a method for computing three-dimensional (3D) atmospheric fronts as surfaces that is capable of extracting continuous and well-confined features suitable for 3D visual analysis, spatio-temporal tracking, and statistical analyses. Recently developed contour-based methods for 3D front extraction rely on computing the third derivative of a moist potential temperature field. Additionally, they require the field to be smoothed to obtain continuous large-scale structures. This paper demonstrates the feasibility of an alternative method to front extraction using ridge surface computation. The proposed method requires only the sec- ond derivative of the input field and produces accurate structures even from unsmoothed data. An application of the ridge-based method to a data set corresponding to Cyclone Friederike demonstrates its benefits and utility towards visual analysis of the full 3D structure of fronts. Y1 - 2024 U6 - https://doi.org/10.1109/VIS55277.2024.00043 SP - 176 EP - 180 ER - TY - CHAP A1 - Klenert, Nicolas A1 - Schwoerer, Finn A1 - Hajarolasvadi, Noushin A1 - Bournez, Siloé A1 - Arlt, Tobias A1 - Mahnke, Heinz-Eberhard A1 - Lepper, Verena A1 - Baum, Daniel T1 - Improving the Identification of Layers in 3D Images of Ancient Papyrus using Artificial Neural Networks T2 - 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Tucson, AZ, USA N2 - The process of digitally unfolding ancient documents, such as folded papyrus packages, from 3D image data aims to be a non-invasive means to make previously hidden writing visible without risking to damage the precious documents. One of the main tasks necessary to digitally unfold a document is the geometric reconstruction of the writing substrate, which is a prerequisite for its subsequent unfolding. All current reconstruction methods require the existence of an interspace between different layers of the document to ensure a correct topology. Layers that appear merged together in the 3D image often result in wrong connections between layers and thus also in a wrong topology of the reconstructed geometry, which hinders the successful unfolding. Here, we propose to use a neural network to facilitate the discrimination of the layers. Using papyrus documents as an example of a particularly difficult writing material, we show that this significantly reduces the number of wrong connections and improves the overall identification of the layers. This in turn enables fully automatic digital unfolding of large areas of highly complex papyrus packages. Utilizing explainable AI (XAI) further allows us to explore the results of the applied neural network. Y1 - 2025 U6 - https://doi.org/10.1109/WACVW65960.2025.00143 SP - 1204 EP - 1212 ER - TY - JOUR A1 - Klenert, Nicolas A1 - Lepper, Verena A1 - Baum, Daniel T1 - A Local Iterative Approach for the Extraction of 2D Manifolds from Strongly Curved and Folded Thin-Layer Structures JF - IEEE Transactions on Visualization and Computer Graphics N2 - Ridge surfaces represent important features for the analysis of 3-dimensional (3D) datasets in diverse applications and are often derived from varying underlying data including flow fields, geological fault data, and point data, but they can also be present in the original scalar images acquired using a plethora of imaging techniques. Our work is motivated by the analysis of image data acquired using micro-computed tomography (μCT) of ancient, rolled and folded thin-layer structures such as papyrus, parchment, and paper as well as silver and lead sheets. From these documents we know that they are 2-dimensional (2D) in nature. Hence, we are particularly interested in reconstructing 2D manifolds that approximate the document’s structure. The image data from which we want to reconstruct the 2D manifolds are often very noisy and represent folded, densely-layered structures with many artifacts, such as ruptures or layer splitting and merging. Previous ridge-surface extraction methods fail to extract the desired 2D manifold for such challenging data. We have therefore developed a novel method to extract 2D manifolds. The proposed method uses a local fast marching scheme in combination with a separation of the region covered by fast marching into two sub-regions. The 2D manifold of interest is then extracted as the surface separating the two sub-regions. The local scheme can be applied for both automatic propagation as well as interactive analysis. We demonstrate the applicability and robustness of our method on both artificial data as well as real-world data including folded silver and papyrus sheets. Y1 - 2024 U6 - https://doi.org/10.1109/TVCG.2023.3327403 VL - 30 IS - 1 SP - 1260 EP - 1270 ER -