@article{MahnkeArltBaumetal., author = {Mahnke, Heinz-Eberhard and Arlt, Tobias and Baum, Daniel and Hege, Hans-Christian and Herter, Felix and Lindow, Norbert and Manke, Ingo and Siopi, Tzulia and Menei, Eve and Etienne, Marc and Lepper, Verena}, title = {Virtual unfolding of folded papyri}, series = {Journal of Cultural Heritage}, volume = {41}, journal = {Journal of Cultural Heritage}, publisher = {Elsevier}, doi = {10.1016/j.culher.2019.07.007}, pages = {264 -- 269}, abstract = {The historical importance of ancient manuscripts is unique since they provide information about the heritage of ancient cultures. Often texts are hidden in rolled or folded documents. Due to recent impro- vements in sensitivity and resolution, spectacular disclosures of rolled hidden texts were possible by X-ray tomography. However, revealing text on folded manuscripts is even more challenging. Manual unfolding is often too risky in view of the fragile condition of fragments, as it can lead to the total loss of the document. X-ray tomography allows for virtual unfolding and enables non-destructive access to hid- den texts. We have recently demonstrated the procedure and tested unfolding algorithms on a mockup sample. Here, we present results on unfolding ancient papyrus packages from the papyrus collection of the Mus{\´e}e du Louvre, among them objects folded along approximately orthogonal folding lines. In one of the packages, the first identification of a word was achieved, the Coptic word for "Lord".}, language = {en} } @misc{MahnkeArltBaumetal., author = {Mahnke, Heinz-Eberhard and Arlt, Tobias and Baum, Daniel and Hege, Hans-Christian and Herter, Felix and Lindow, Norbert and Manke, Ingo and Siopi, Tzulia and Menei, Eve and Etienne, Marc and Lepper, Verena}, title = {Virtual unfolding of folded papyri}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-74338}, abstract = {The historical importance of ancient manuscripts is unique since they provide information about the heritage of ancient cultures. Often texts are hidden in rolled or folded documents. Due to recent impro- vements in sensitivity and resolution, spectacular disclosures of rolled hidden texts were possible by X-ray tomography. However, revealing text on folded manuscripts is even more challenging. Manual unfolding is often too risky in view of the fragile condition of fragments, as it can lead to the total loss of the document. X-ray tomography allows for virtual unfolding and enables non-destructive access to hid- den texts. We have recently demonstrated the procedure and tested unfolding algorithms on a mockup sample. Here, we present results on unfolding ancient papyrus packages from the papyrus collection of the Mus{\´e}e du Louvre, among them objects folded along approximately orthogonal folding lines. In one of the packages, the first identification of a word was achieved, the Coptic word for "Lord".}, language = {en} } @article{BaumHerterLarsenetal., author = {Baum, Daniel and Herter, Felix and Larsen, John M{\o}ller and Lichtenberger, Achim and Raja, Rubina}, title = {Revisiting the Jerash Silver Scroll: a new visual data analysis approach}, series = {Digital Applications in Archaeology and Cultural Heritage}, volume = {21}, journal = {Digital Applications in Archaeology and Cultural Heritage}, doi = {10.1016/j.daach.2021.e00186}, pages = {e00186}, abstract = {This article revisits a complexly folded silver scroll excavated in Jerash, Jordan in 2014 that was digitally examined in 2015. In this article we apply, examine and discuss a new virtual unfolding technique that results in a clearer image of the scroll's 17 lines of writing. We also compare it to the earlier unfolding and discuss progress in general analytical tools. We publish the original and the new images as well as the unfolded volume data open access in order to make these available to researchers interested in optimising unfolding processes of various complexly folded materials.}, language = {en} } @misc{BaumHerterLepper, author = {Baum, Daniel and Herter, Felix and Lepper, Verena}, title = {Jerash Silver Scroll: Virtually Unfolded Volume}, series = {figshare}, journal = {figshare}, doi = {10.6084/m9.figshare.12145236}, abstract = {A new virtual unfolding technique was applied to a silver scroll excavated in Jerash, Jordan, in 2014. As result of the unfolding, 17 lines of writing are clearly visible in the unfolded volumetric data that is published here.}, language = {en} } @misc{Herter2018, type = {Master Thesis}, author = {Herter, Felix}, title = {Supervised Classification of Microtubule Ends: An Evaluation of Machine Learning Approaches}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-68395}, year = {2018}, abstract = {Aim of this thesis was to evaluate the performance of three popular machine learning methods - decision trees, support vector machines, and neural networks - on a supervised image classification task from the domain of cell biology. Specifically, the task was to classify microtubule ends in electron tomography images as open or closed. Microtubules are filamentous macromolecules of the cytoskeleton. Distribution of their end types is of interest to cell biologists as it allows to analyze microtubule nucleation sites. Currently classification is done manually by domain experts, which is a difficult task due to the low signal-to-noise ratio and the abundance of microtubules in a single cell. Automating this tedious and error prone task would be beneficial to both efficiency and consistency. Images of microtubule ends were obtained from electron tomography reconstructions of mitotic spindles. As ground truth data for training and testing four independent expert classifications for the same samples from different tomograms were used. Image information around microtubule ends was extracted in various formats for further processing. For all classifiers we considered how the performance varies when different preprocessing techniques (per-feature and per-image standardization) are applied. or decision trees and support vector machines we also evaluated the effect of training on a) imbalanced versus under- and over-sampled data and b) image-based vs feature-based input for specifically designed features. The results show that for decision trees and support vector machines classification on features outperforms classification on images. Both methods give most equalized per-class accuracies when the training data was undersampled and when preprocessed with per-image standardization prior to features extraction. Neural networks gave the best results when no preprocessing was applied. The final decision tree, support vector machine, and neural network obtained accuracies on the test set for (open,closed ) samples of (62\%, 72\%), (66\%, 70\%), and (61\%, 78\%) respectively, when considering all samples where at least one expert assigned a label. Restricting the test set to samples with at least three agreeing expert labels raised these to (78\%, 84\%), (74\%, 92\%), and (82\%, 88\%). It can be observed that many samples misclassified by the algorithms were also difficult to classify for the experts.}, language = {en} } @article{HerterHegeHadwigeretal., author = {Herter, Felix and Hege, Hans-Christian and Hadwiger, Markus and Lepper, Verena and Baum, Daniel}, title = {Thin-Volume Visualization on Curved Domains}, series = {Computer Graphics Forum}, volume = {40}, journal = {Computer Graphics Forum}, number = {3}, publisher = {Wiley-Blackwell Publishing Ltd.}, address = {United Kingdom}, doi = {10.1111/cgf.14296}, pages = {147 -- 157}, abstract = {Thin, curved structures occur in many volumetric datasets. Their analysis using classical volume rendering is difficult because parts of such structures can bend away or hide behind occluding elements. This problem cannot be fully compensated by effective navigation alone, because structure-adapted navigation in the volume is cumbersome and only parts of the structure are visible in each view. We solve this problem by rendering a spatially transformed view into the volume so that an unobscured visualization of the entire curved structure is obtained. As a result, simple and intuitive navigation becomes possible. The domain of the spatial transform is defined by a triangle mesh that is topologically equivalent to an open disc and that approximates the structure of interest. The rendering is based on ray-casting in which the rays traverse the original curved sub-volume. In order to carve out volumes of varying thickness, the lengths of the rays as well as the position of the mesh vertices can be easily modified in a view-controlled manner by interactive painting. We describe a prototypical implementation and demonstrate the interactive visual inspection of complex structures from digital humanities, biology, medicine, and materials science. Displaying the structure as a whole enables simple inspection of interesting substructures in their original spatial context. Overall, we show that transformed views utilizing ray-casting-based volume rendering supported by guiding surface meshes and supplemented by local, interactive modifications of ray lengths and vertex positions, represent a simple but versatile approach to effectively visualize thin, curved structures in volumetric data.}, language = {en} }