@article{FroehlerElberfeldMoelleretal., author = {Fr{\"o}hler, Bernhard and Elberfeld, Tim and M{\"o}ller, Torsten and Hege, Hans-Christian and Weissenb{\"o}ck, Johannes and De Beenhouwer, Jan and Sijbers, Jan and Kastner, Johann and Heinzl, Christoph}, title = {A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science}, series = {Computer Graphics Forum}, volume = {38}, journal = {Computer Graphics Forum}, number = {3}, doi = {10.1111/cgf.13688}, pages = {273 -- 283}, abstract = {We present visual analysis methods for the evaluation of tomographic fiber reconstruction algorithms by means of analysis, visual debugging and comparison of reconstructed fibers in materials science. The methods are integrated in a tool (FIAKER) that supports the entire workflow. It enables the analysis of various fiber reconstruction algorithms, of differently parameterized fiber reconstruction algorithms and of individual steps in iterative fiber reconstruction algorithms. Insight into the performance of fiber reconstruction algorithms is obtained by a list-based ranking interface. A 3D view offers interactive visualization techniques to gain deeper insight, e.g., into the aggregated quality of the examined fiber reconstruction algorithms and parameterizations. The tool was designed in close collaboration with researchers who work with fiber-reinforced polymers on a daily basis and develop algorithms for tomographic reconstruction and characterization of such materials. We evaluate the tool using synthetic datasets as well as tomograms of real materials. Five case studies certify the usefulness of the tool, showing that it significantly accelerates the analysis and provides valuable insights that make it possible to improve the fiber reconstruction algorithms. The main contribution of the paper is the well-considered combination of methods and their seamless integration into a visual tool that supports the entire workflow. Further findings result from the analysis of (dis-)similarity measures for fibers as well as from the discussion of design decisions. It is also shown that the generality of the analytical methods allows a wider range of applications, such as the application in pore space analysis.}, language = {en} } @inproceedings{FroehlerdaCunhaMeloWeissenboecketal., author = {Fr{\"o}hler, Bernhard and da Cunha Melo, Lucas and Weissenb{\"o}ck, Johannes and Kastner, Johann and M{\"o}ller, Torsten and Hege, Hans-Christian and Gr{\"o}ller, Eduard M. and Sanctorum, Jonathan and De Beenhouwer, Jan and Sijbers, Jan and Heinzl, Christoph}, title = {Tools for the analysis of datasets from X-ray computed tomography based on Talbot-Lau grating interferometry}, series = {Proceedings of iCT 2019, (9th Conference on Industrial Computed Tomography, Padova, Italy - iCT 2019, February 13-15, 2019)}, booktitle = {Proceedings of iCT 2019, (9th Conference on Industrial Computed Tomography, Padova, Italy - iCT 2019, February 13-15, 2019)}, number = {paper 52}, pages = {8}, abstract = {This work introduces methods for analyzing the three imaging modalities delivered by Talbot-Lau grating interferometry X-ray computed tomography (TLGI-XCT). The first problem we address is providing a quick way to show a fusion of all three modal- ities. For this purpose the tri-modal transfer function widget is introduced. The widget controls a mixing function that uses the output of the transfer functions of all three modalities, allowing the user to create one customized fused image. A second problem prevalent in processing TLGI-XCT data is a lack of tools for analyzing the segmentation process of such multimodal data. We address this by providing methods for computing three types of uncertainty: From probabilistic segmentation algorithms, from the voxel neighborhoods as well as from a collection of results. We furthermore introduce a linked views interface to explore this data. The techniques are evaluated on a TLGI-XCT scan of a carbon-fiber reinforced dataset with impact damage. We show that the transfer function widget accelerates and facilitates the exploration of this dataset, while the uncertainty analysis methods give insights into how to tweak and improve segmentation algorithms for more suitable results.}, language = {en} } @article{FroehlerElberfeldMoelleretal., author = {Fr{\"o}hler, Bernhard and Elberfeld, Tim and M{\"o}ller, Torsten and Hege, Hans-Christian and De Beenhouwer, Jan and Sijbers, Jan and Kastner, Johann and Heinzl, Christoph}, title = {Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres}, series = {Nondestructive Testing and Evaluation}, volume = {35}, journal = {Nondestructive Testing and Evaluation}, number = {3}, doi = {10.1080/10589759.2020.1774583}, pages = {328 -- 341}, abstract = {We present visual methods for the analysis and comparison of the results of curved fibre reconstruction algorithms, i.e., of algorithms extracting characteristics of curved fibres from X-ray computed tomography scans. In this work, we extend previous methods for the analysis and comparison of results of different fibre reconstruction algorithms or parametrisations to the analysis of curved fibres. We propose fibre dissimilarity measures for such curved fibres and apply these to compare multiple results to a specified reference. We further propose visualisation methods to analyse differences between multiple results quantitatively and qualitatively. In two case studies, we show that the presented methods provide valuable insights for advancing and parametrising fibre reconstruction algorithms, and support in improving their results in characterising curved fibres.}, language = {en} }