5 Search Results for "Maciejewski, Ross"

Document
DOI: 10.4230/LIPIcs.GD.2025.11
Towards a Better Understanding of Graph Perception in Immersive Environments

Authors: Lin Zhang, Yao Wang, Ying Zhang, Wilhelm Kerle-Malcharek, Karsten Klein, Falk Schreiber, and Andreas Bulling

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
As Immersive Analytics (IA) increasingly uses Virtual Reality (VR) for stereoscopic 3D (S3D) graph visualisation, it is crucial to understand how users perceive network structures in these immersive environments. However, little is known about how humans read S3D graphs during task solving, and how gaze behaviour indicates task performance. To address this gap, we report a user study with 18 participants asked to perform three analytical tasks on S3D graph visualisations in a VR environment. Our findings reveal systematic relationships between network structural properties and gaze behaviour. Based on these insights, we contribute a comprehensive eye tracking methodology for analysing human perception in immersive environments and establish eye tracking as a valuable tool for objectively evaluating cognitive load in S3D graph visualisation.
Cite as

Lin Zhang, Yao Wang, Ying Zhang, Wilhelm Kerle-Malcharek, Karsten Klein, Falk Schreiber, and Andreas Bulling. Towards a Better Understanding of Graph Perception in Immersive Environments. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 11:1-11:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zhang_et_al:LIPIcs.GD.2025.11,
  author =	{Zhang, Lin and Wang, Yao and Zhang, Ying and Kerle-Malcharek, Wilhelm and Klein, Karsten and Schreiber, Falk and Bulling, Andreas},
  title =	{{Towards a Better Understanding of Graph Perception in Immersive Environments}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{11:1--11:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.11},
  URN =		{urn:nbn:de:0030-drops-249976},
  doi =		{10.4230/LIPIcs.GD.2025.11},
  annote =	{Keywords: Stereoscopic 3D, Graph Visualisation, Eye Tracking, Graph Perception}
}
Document
DOI: 10.4230/LIPIcs.GD.2025.7
Same Quality Metrics, Different Graph Drawings

Authors: Simon van Wageningen, Tamara Mchedlidze, and Alexandru C. Telea

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
Graph drawings are commonly used to visualize relational data. User understanding and performance are linked to the quality of such drawings, which is measured by quality metrics. The tacit knowledge in the graph drawing community about these quality metrics is that they are not always able to accurately capture the quality of graph drawings. In particular, such metrics may rate drawings with very poor quality as very good. In this work we make this tacit knowledge explicit by showing that we can modify existing graph drawings into arbitrary target shapes while keeping one or more quality metrics almost identical. This supports the claim that more advanced quality metrics are needed to capture the "goodness" of a graph drawing and that we cannot confidently rely on the value of a single (or several) certain quality metrics.
Cite as

Simon van Wageningen, Tamara Mchedlidze, and Alexandru C. Telea. Same Quality Metrics, Different Graph Drawings. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 7:1-7:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{vanwageningen_et_al:LIPIcs.GD.2025.7,
  author =	{van Wageningen, Simon and Mchedlidze, Tamara and Telea, Alexandru C.},
  title =	{{Same Quality Metrics, Different Graph Drawings}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{7:1--7:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.7},
  URN =		{urn:nbn:de:0030-drops-249935},
  doi =		{10.4230/LIPIcs.GD.2025.7},
  annote =	{Keywords: graph drawing, quality metrics, assumptions, fooling}
}
Document
DOI: 10.4230/LIPIcs.GD.2025.27
Geometry Matters in Planar Storyplans

Authors: Alexander Dobler, Maximilian Holzmüller, and Martin Nöllenburg

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
A storyplan visualizes a graph G = (V,E) as a sequence of 𝓁 frames Γ₁, … , Γ_𝓁, each of which is a drawing of the induced subgraph G[V_i] of a vertex subset V_i ⊆ V. Moreover, each vertex v ∈ V is contained in a single consecutive sequence of frames Γ_i, … , Γ_j, all vertices and edges contained in consecutive frames are drawn identically, and the union of all frames is a drawing of G. In GD 2022, the concept of planar storyplans was introduced, in which each frame must be a planar (topological) drawing. Several (parameterized) complexity results for recognizing graphs that admit a planar storyplan were provided, including NP-hardness. In this paper, we investigate an open question posed in the GD paper and show that the geometric and topological settings of the planar storyplan problem differ: We provide an instance of a graph that admits a planar storyplan, but no planar geometric storyplan, in which each frame is a planar straight-line drawing. Still, by adapting the reduction proof from the topological to the geometric setting, we show that recognizing the graphs that admit planar geometric storyplans remains NP-hard.
Cite as

Alexander Dobler, Maximilian Holzmüller, and Martin Nöllenburg. Geometry Matters in Planar Storyplans. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 27:1-27:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{dobler_et_al:LIPIcs.GD.2025.27,
  author =	{Dobler, Alexander and Holzm\"{u}ller, Maximilian and N\"{o}llenburg, Martin},
  title =	{{Geometry Matters in Planar Storyplans}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{27:1--27:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.27},
  URN =		{urn:nbn:de:0030-drops-250135},
  doi =		{10.4230/LIPIcs.GD.2025.27},
  annote =	{Keywords: geometric storyplan, planarity, straight-line drawing, dynamic graph drawing}
}
Document
DOI: 10.4230/OASIcs.iPMVM.2020.19
Interpolation of Scientific Image Databases

Authors: Eric Georg Kinner, Jonas Lukasczyk, David Honegger Rogers, Ross Maciejewski, and Christoph Garth

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
This paper explores how recent convolutional neural network (CNN)-based techniques can be used to interpolate images inside scientific image databases. These databases are frequently used for the interactive visualization of large-scale simulations, where images correspond to samples of the parameter space (e.g., timesteps, isovalues, thresholds, etc.) and the visualization space (e.g., camera locations, clipping planes, etc.). These databases can be browsed post hoc along the sampling axis to emulate real-time interaction with large-scale datasets. However, the resulting databases are limited to their contained images, i.e., the sampling points. In this paper, we explore how efficiently and accurately CNN-based techniques can derive new images by interpolating database elements. We demonstrate on several real-world examples that the size of databases can be further reduced by dropping samples that can be interpolated post hoc with an acceptable error, which we measure qualitatively and quantitatively.
Cite as

Eric Georg Kinner, Jonas Lukasczyk, David Honegger Rogers, Ross Maciejewski, and Christoph Garth. Interpolation of Scientific Image Databases. In 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{kinner_et_al:OASIcs.iPMVM.2020.19,
  author =	{Kinner, Eric Georg and Lukasczyk, Jonas and Rogers, David Honegger and Maciejewski, Ross and Garth, Christoph},
  title =	{{Interpolation of Scientific Image Databases}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{19:1--19:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.19},
  URN =		{urn:nbn:de:0030-drops-137686},
  doi =		{10.4230/OASIcs.iPMVM.2020.19},
  annote =	{Keywords: Image Interpolation, Image Database, Cinema Database}
}
Document
DOI: 10.4230/DFU.Vol2.SciViz.2011.212
Abstract Feature Space Representation for Volumetric Transfer Function Exploration

Authors: Ross Maciejewski, Yun Jang, David S. Ebert, and Kelly P. Gaither

Published in: Dagstuhl Follow-Ups, Volume 2, Scientific Visualization: Interactions, Features, Metaphors (2011)

Abstract
The application of n-dimensional transfer functions for feature segmentation has become increasingly popular in volume rendering. Recent work has focused on the utilization of higher order dimensional transfer functions incorporating spatial dimensions (x,y, and z) along with traditional feature space dimensions (value and value gradient). However, as the dimensionality increases, it becomes exceedingly difficult to abstract the transfer function into an intuitive and interactive workspace. In this work we focus on populating the traditional two-dimensional histogram with a set of derived metrics from the spatial (x, y and z) and feature space (value, value gradient, etc.) domain to create a set of abstract feature space transfer function domains. Current two-dimensional transfer function widgets typically consist of a two-dimensional histogram where each entry in the histogram represents the number of voxels that maps to that entry. In the case of an abstract transfer function design, the amount of spatial variance at that histogram coordinate is mapped instead, thereby relating additional information about the data abstraction in the projected space. Finally, a non-parametric kernel density estimation approach for feature space clustering is applied in the abstracted space, and the resultant transfer functions are discussed with respect to the space abstraction.
Cite as

Ross Maciejewski, Yun Jang, David S. Ebert, and Kelly P. Gaither. Abstract Feature Space Representation for Volumetric Transfer Function Exploration. In Scientific Visualization: Interactions, Features, Metaphors. Dagstuhl Follow-Ups, Volume 2, pp. 212-221, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InCollection{maciejewski_et_al:DFU.Vol2.SciViz.2011.212,
  author =	{Maciejewski, Ross and Jang, Yun and Ebert, David S. and Gaither, Kelly P.},
  title =	{{Abstract Feature Space Representation for Volumetric Transfer Function Exploration}},
  booktitle =	{Scientific Visualization: Interactions, Features, Metaphors},
  pages =	{212--221},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-26-2},
  ISSN =	{1868-8977},
  year =	{2011},
  volume =	{2},
  editor =	{Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol2.SciViz.2011.212},
  URN =		{urn:nbn:de:0030-drops-32955},
  doi =		{10.4230/DFU.Vol2.SciViz.2011.212},
  annote =	{Keywords: Volumetric Transfer Function, Abstract Feature Space}
}
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