GEMSe: Visualization‐Guided Exploration of Multi‐channel Segmentation Algorithms

Fröhler, Bernhard; Möller, Torsten B.; Heinzl, Christoph

doi:10.1111/cgf.12895

Cited by 20 publications

(20 citation statements)

References 24 publications

(27 reference statements)

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“…Our users unanimously were asking for a 3D view and were thrilled to see its possibilities for visualizing the results when they first saw the prototype. This was surprising and contrary to the experience from previous work on the analysis of image processing tools in material sciences (e.g., for analyzing image segmentation results [FMH16]). The preference for 3D in this case comes, on the one hand, from the fact that we have polygonal data, not volumes, where slice images are easier to comprehend than direct volume rendering that is prone to occlusion.…”

Section: Discussion Of Design Decisions and Lessons Learnedcontrasting

confidence: 88%

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

Fröhler

Elberfeld

Möller

et al. 2019

Computer Graphics Forum

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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.

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Section: Discussion Of Design Decisions and Lessons Learnedcontrasting

confidence: 88%

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

Fröhler

Elberfeld

Möller

et al. 2019

Computer Graphics Forum

Self Cite

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“…• The 4DCT tools (Amirkhanov et al, 2016) • Fuzzy Feature Tracking (Reh, Amirkhanov, Heinzl, Kastner, & Gröller, 2015) provides graphs for tracking the creation, continuation, and merge of defects between different stages of fatigue testing. • GEMSe (Fröhler, Möller, & Heinzl, 2016) supports users in finding optimal parameters for their volume segmentation tasks without requiring a ground truth. • The PorosityAnalyzer (Weissenböck, Amirkhanov, Gröller, Kastner, & Heinzl, 2016) similarly supports users in finding the ideal segmentation algorithm and parameterization when they are determining porosity values, e.g., in fiber-reinforced polymers.…”

Section: Discussionmentioning

confidence: 99%

open_iA: A tool for processing and visual analysis of industrial computed tomography datasets

Fröhler¹,

Weissenböck²,

Schiwarth³

et al. 2019

JOSS

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“…In most cases, the visualization tools cover at least the target group of domain experts/practitioners [EGG*12, FMH16, FCS*20, GNRM08, HNH*12, KPN16]. Then, other target groups such as ML experts [JC17b, KJR*18, SSK10, WLN*17] and developers are in the focus of the authors [KFC16, Mad19, RL15b, YZR*18] (commonly together).…”

Section: In‐depth Categorization Of Trust Against Facets Of Interamentioning

confidence: 99%

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Chatzimparmpas

Martins

Jusufi

et al. 2020

Computer Graphics Forum

121

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Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State‐of‐the‐Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web‐based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

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GEMSe: Visualization‐Guided Exploration of Multi‐channel Segmentation Algorithms

Cited by 20 publications

References 24 publications

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

open_iA: A tool for processing and visual analysis of industrial computed tomography datasets

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

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