EuroVisPosters2022

Permanent URI for this collection

Posters
Scientific Convergence and Divergence in Visualization and Visual Analytics
Jiangen He
GDot-i: Interactive System for Dot Paintings of Graphs
Peter Eades, Seok-Hee Hong, Martin McGrane, and Amyra Meidiana
Digital Twins of Smart Farms
Yuhang Zhao, Zheyu Jiang, Shanchen Pang, and Zhihan Lv
Automatic Segmentation of Tooth Images: Optimization of Multi-parameter Image Processing Workflow
Giovani Bressan Fogalli, Sérgio Roberto Peres Line, and Daniel Baum
Explorative Visual Analysis of Spatio-temporal Regions to Detect Hemodynamic Biomarker Candidates
Adrian Derstroff, Simon Leistikow, Ali Nahardani, Mahyasadat Ebrahimi, Verena Hoerr, and Lars Linsen
Visually Explaining Publication Ranks in Citation-based Literature Search with PURE Suggest
Fabian Beck and Cedric Krause
Visualizing the Evolution of Multi-agent Game-playing Behaviors
Shivam Agarwal, Shahid Latif, Aristide Rothweiler, and Fabian Beck
Visual Exploration of Genetic Sequence Variants in Pangenomes
Astrid van den Brandt, Eef M. Jonkheer, Dirk-Jan M. van Workum, Sandra Smit, and Anna Vilanova
Interactive Visualization of Machine Learning Model Results Predicting Infection Risk
Steffen Schäfer, Tom Baumgartl, Antje Wulff, Arjan Kuijper, Michael Marschollek, Simone Scheithauer, and Tatiana von Landesberger
A Design Space for Explainable Ranking and Ranking Models
Ibrahim Al Hazwan, Jenny Schmid, Madhav Sachdeva, and Jürgen Bernard
Visual Queries on Bipartite Multivariate Dynamic Social Networks
Alexis Pister, Christophe Prieur, and Jean-Daniel Fekete
MOBS - Multi-Omics Brush for Subgraph Visualisation
Dries Heylen, Jannes Peeters, Gökhan Ertaylan, Jef Hooyberghs, and Jan Aerts
A Mental Workload Estimation for Visualization Evaluation Using EEG Data and NASA-TLX
Soobin Yim, ChanYoung Yoon, Sangbong Yoo, and Yun Jang
Validating Perception of Hyperspectral Textures in Virtual Reality Systems
Francisco Díaz-Barrancas, Halina Cwierz, Raquel Gil-Rodríguez, and Pedro J. Pardo
Situated Visualization in Motion for Video Games
Federica Bucchieri, Lijie Yao, and Petra Isenberg
Using Data Comics to Enhance Visualization Literacy
Magdalena Boucher, Christina Stoiber, and Wolfgang Aigner
Accurate Molecular Atom Selection in VR
Elena Molina and Pere-Pau Vázquez
Context Specific Visualizations on Smartwatches
Alaul Islam, Tanja Blascheck, and Petra Isenberg
Visual Exploration of Preference-based Routes in Ski Resorts
Julius Rauscher, Matthias Miller, and Daniel A. Keim
Visualizing Prediction Provenance in Regression Random Forests
Nicolas Médoc, Vasile Ciorna, Frank Petry, and Mohammad Ghoniem
Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems
Dario Benvenuti, Giovanni Fiordeponti, Hao Cheng, Tiziana Catarci, Jean-Daniel Fekete, Giuseppe Santucci, Marco Angelini, and Leilani Battle
ANARI: ANAlytic Rendering Interface
Kevin Griffin, Jefferson Amstutz, Dave DeMarle, Johannes Günther, Jakob Progsch, William Sherman, John E. Stone, Will Usher, and Kees van Kooten
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback
Yuncong Yu, Dylan Kruyff, Jiao Jiao, Tim Becker, and Michael Behrisch
Exploration and Analysis of Image-base Simulation Ensembles
Mai Dahshan, Terece L. Turton, and Nicholas Polys
Visualizing Similarities between American Rap-Artists
Christofer Meinecke, Jeremias Schebera, Jakob Eschrich, and Daniel Wiegreffe
Interactive Attribution-based Explanations for Image Segmentation
Christina Humer, Mohamed Elharty, Andreas Hinterreiter, and Marc Streit
VisualBib(va): A Visual Analytics Platform for Authoring and Reviewing Bibliographies
Antonina Dattolo, Marco Corbatto, and Marco Angelini
Visualization Challenges of Variant Interpretation in Multiscale NGS Data
Emilia Ståhlbom, Jesper Molin, Claes Lundström, and Anders Ynnerman
Sustainable Urban Wastewater Treatment Visualizations
Juan Marin Vega, Nerea Uri-Carreño, Jakob Kusnick, and Stefan Jänicke
A Case Study on Implementing Screen Reader Accessibility in Dynamic Visualizations
Rita Costa, Beatriz Malveiro, João Palmeiro, and Pedro Bizarro
On Visualizing Music Storage Media for Modern Access to Historic Sources
Richard Khulusi and Heike Fricke
Enhancing Evaluation of Room Scale VR Studies to POI Visualizations in Minimaps
Batoul Ajdadilish, Steffi Kohl, and Kay Schröder
Chord2DS: An Extension to Chord Diagram to Show Data Elements from Two Heterogeneous Data Sources
Shah Rukh Humayoun and Likhitha Brahmadevara
Parameter Sensitivity and Uncertainty Visualization in DTI
Faizan Siddiqui, Thomas Höllt, and Anna Vilanova

BibTeX (EuroVisPosters2022)
@inproceedings{
10.2312:evp.20221105,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Scientific Convergence and Divergence in Visualization and Visual Analytics}},
author = {
He, Jiangen
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221105}
}
@inproceedings{
10.2312:evp.20221107,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Digital Twins of Smart Farms}},
author = {
Zhao, Yuhang
 and
Jiang, Zheyu
 and
Pang, Shanchen
 and
Lv, Zhihan
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221107}
}
@inproceedings{
10.2312:evp.20221106,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
GDot-i: Interactive System for Dot Paintings of Graphs}},
author = {
Eades, Peter
 and
Hong, Seok-Hee
 and
McGrane, Martin
 and
Meidiana, Amyra
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221106}
}
@inproceedings{
10.2312:evp.20222011,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
EuroVis 2022 Posters: Frontmatter}},
author = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20222011}
}
@inproceedings{
10.2312:evp.20221108,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Automatic Segmentation of Tooth Images: Optimization of Multi-parameter Image Processing Workflow}},
author = {
Bressan Fogalli, Giovani
 and
Line, Sérgio Roberto Peres
 and
Baum, Daniel
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221108}
}
@inproceedings{
10.2312:evp.20221109,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Explorative Visual Analysis of Spatio-temporal Regions to Detect Hemodynamic Biomarker Candidates}},
author = {
Derstroff, Adrian
 and
Leistikow, Simon
 and
Nahardani, Ali
 and
Ebrahimi, Mahyasadat
 and
Hoerr, Verena
 and
Linsen, Lars
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221109}
}
@inproceedings{
10.2312:evp.20221112,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visual Exploration of Genetic Sequence Variants in Pangenomes}},
author = {
van den Brandt, Astrid
 and
Jonkheer, Eef M.
 and
van Workum, Dirk-Jan M.
 and
Smit, Sandra
 and
Vilanova, Anna
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221112}
}
@inproceedings{
10.2312:evp.20221111,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visualizing the Evolution of Multi-agent Game-playing Behaviors}},
author = {
Agarwal, Shivam
 and
Latif, Shahid
 and
Rothweiler, Aristide
 and
Beck, Fabian
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221111}
}
@inproceedings{
10.2312:evp.20221110,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visually Explaining Publication Ranks in Citation-based Literature Search with PURE Suggest}},
author = {
Beck, Fabian
 and
Krause, Cedric
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221110}
}
@inproceedings{
10.2312:evp.20221114,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
A Design Space for Explainable Ranking and Ranking Models}},
author = {
Hazwan, Ibrahim Al
 and
Schmid, Jenny
 and
Sachdeva, Madhav
 and
Bernard, Jürgen
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221114}
}
@inproceedings{
10.2312:evp.20221113,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Interactive Visualization of Machine Learning Model Results Predicting Infection Risk}},
author = {
Schäfer, Steffen
 and
Baumgartl, Tom
 and
Wulff, Antje
 and
Kuijper, Arjan
 and
Marschollek, Michael
 and
Scheithauer, Simone
 and
von Landesberger, Tatiana
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221113}
}
@inproceedings{
10.2312:evp.20221115,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visual Queries on Bipartite Multivariate Dynamic Social Networks}},
author = {
Pister, Alexis
 and
Prieur, Christophe
 and
Fekete, Jean-Daniel
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221115}
}
@inproceedings{
10.2312:evp.20221116,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
MOBS - Multi-Omics Brush for Subgraph Visualisation}},
author = {
Heylen, Dries
 and
Peeters, Jannes
 and
Ertaylan, Gökhan
 and
Hooyberghs, Jef
 and
Aerts, Jan
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221116}
}
@inproceedings{
10.2312:evp.20221117,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
A Mental Workload Estimation for Visualization Evaluation Using EEG Data and NASA-TLX}},
author = {
Yim, Soobin
 and
Yoon, Chanyoung
 and
Yoo, Sangbong
 and
Jang, Yun
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221117}
}
@inproceedings{
10.2312:evp.20221118,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Validating Perception of Hyperspectral Textures in Virtual Reality Systems}},
author = {
Díaz-Barrancas, Francisco
 and
Cwierz, Halina
 and
Gil-Rodríguez, Raquel
 and
Pardo, Pedro J.
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221118}
}
@inproceedings{
10.2312:evp.20221119,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Situated Visualization in Motion for Video Games}},
author = {
Bucchieri, Federica
 and
Yao, Lijie
 and
Isenberg, Petra
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221119}
}
@inproceedings{
10.2312:evp.20221120,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Using Data Comics to Enhance Visualization Literacy}},
author = {
Boucher, Magdalena
 and
Stoiber, Christina
 and
Aigner, Wolfgang
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221120}
}
@inproceedings{
10.2312:evp.20221121,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Accurate Molecular Atom Selection in VR}},
author = {
Molina, Elena
 and
Vázquez, Pere-Pau
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221121}
}
@inproceedings{
10.2312:evp.20221123,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visual Exploration of Preference-based Routes in Ski Resorts}},
author = {
Rauscher, Julius
 and
Miller, Matthias
 and
Keim, Daniel A.
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221123}
}
@inproceedings{
10.2312:evp.20221122,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Context Specific Visualizations on Smartwatches}},
author = {
Islam, Alaul
 and
Blascheck, Tanja
 and
Isenberg, Petra
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221122}
}
@inproceedings{
10.2312:evp.20221125,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems}},
author = {
Benvenuti, Dario
 and
Fiordeponti, Giovanni
 and
Cheng, Hao
 and
Catarci, Tiziana
 and
Fekete, Jean-Daniel
 and
Santucci, Giuseppe
 and
Angelini, Marco
 and
Battle, Leilani
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221125}
}
@inproceedings{
10.2312:evp.20221124,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visualizing Prediction Provenance in Regression Random Forests}},
author = {
Médoc, Nicolas
 and
Ciorna, Vasile
 and
Petry, Frank
 and
Ghoniem, Mohammad
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221124}
}
@inproceedings{
10.2312:evp.20221127,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback}},
author = {
Yu, Yuncong
 and
Kruyff, Dylan
 and
Jiao, Jiao
 and
Becker, Tim
 and
Behrisch, Michael
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221127}
}
@inproceedings{
10.2312:evp.20221126,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
ANARI: ANAlytic Rendering Interface}},
author = {
Griffin, Kevin
 and
Amstutz, Jefferson
 and
DeMarle, Dave
 and
Günther, Johannes
 and
Progsch, Jakob
 and
Sherman, William
 and
Stone, John E.
 and
Usher, Will
 and
Kooten, Kees van
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221126}
}
@inproceedings{
10.2312:evp.20221129,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visualizing Similarities between American Rap-Artists}},
author = {
Meinecke, Christofer
 and
Schebera, Jeremias
 and
Eschrich, Jakob
 and
Wiegreffe, Daniel
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221129}
}
@inproceedings{
10.2312:evp.20221128,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Exploration and Analysis of Image-base Simulation Ensembles}},
author = {
Dahshan, Mai
 and
Turton, Terece L.
 and
Polys, Nicholas
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221128}
}
@inproceedings{
10.2312:evp.20221131,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
VisualBib(va): A Visual Analytics Platform for Authoring and Reviewing Bibliographies}},
author = {
Dattolo, Antonina
 and
Corbatto, Marco
 and
Angelini, Marco
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221131}
}
@inproceedings{
10.2312:evp.20221133,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Sustainable Urban Wastewater Treatment Visualizations}},
author = {
Vega, Juan Marin
 and
Uri-Carreño, Nerea
 and
Kusnick, Jakob
 and
Jänicke, Stefan
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221133}
}
@inproceedings{
10.2312:evp.20221132,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Visualization Challenges of Variant Interpretation in Multiscale NGS Data}},
author = {
Ståhlbom, Emilia
 and
Molin, Jesper
 and
Lundström, Claes
 and
Ynnerman, Anders
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221132}
}
@inproceedings{
10.2312:evp.20221130,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Interactive Attribution-based Explanations for Image Segmentation}},
author = {
Humer, Christina
 and
Elharty, Mohamed
 and
Hinterreiter, Andreas
 and
Streit, Marc
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221130}
}
@inproceedings{
10.2312:evp.20221134,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
A Case Study on Implementing Screen Reader Accessibility in Dynamic Visualizations}},
author = {
Costa, Rita
 and
Malveiro, Beatriz
 and
Palmeiro, João
 and
Bizarro, Pedro
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221134}
}
@inproceedings{
10.2312:evp.20221135,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
On Visualizing Music Storage Media for Modern Access to Historic Sources}},
author = {
Khulusi, Richard
 and
Fricke, Heike
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221135}
}
@inproceedings{
10.2312:evp.20221136,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Enhancing Evaluation of Room Scale VR Studies to POI Visualizations in Minimaps}},
author = {
Ajdadilish, Batoul
 and
Kohl, Steffi
 and
Schröder, Kay
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221136}
}
@inproceedings{
10.2312:evp.20221137,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Chord2DS: An Extension to Chord Diagram to Show Data Elements from Two Heterogeneous Data Sources}},
author = {
Humayoun, Shah Rukh
 and
Brahmadevara, Likhitha
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221137}
}
@inproceedings{
10.2312:evp.20221138,
booktitle = {
EuroVis 2022 - Posters},
editor = {
Krone, Michael
 and
Lenti, Simone
 and
Schmidt, Johanna
}, title = {{
Parameter Sensitivity and Uncertainty Visualization in DTI}},
author = {
Siddiqui, Faizan
 and
Höllt, Thomas
 and
Vilanova, Anna
}, year = {
2022},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {
10.2312/evp.20221138}
}

Browse

Recent Submissions

Now showing 1 - 35 of 35
  • Item
    Scientific Convergence and Divergence in Visualization and Visual Analytics
    (The Eurographics Association, 2022) He, Jiangen; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    We present preliminary results of a visualization tool designed to visualize scientific evolution by using scientific publication data, especially convergence-divergence processes. It aims to increase the efficiency and accuracy of our understanding of scientific knowledge in a certain field with limited domain knowledge. We visualized 2,435 papers published in IEEE VIS and EuroVis to demonstrate the tool and provide a big picture of the scientific evolution in the visualization community.
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    Digital Twins of Smart Farms
    (The Eurographics Association, 2022) Zhao, Yuhang; Jiang, Zheyu; Pang, Shanchen; Lv, Zhihan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    In recent years, the development of Digital Twins has made rapid progress, and Digital Twins has gradually begun to combine various fields and applied to the current digitalization of the physical world. Digital Twins can play an important role in agriculture. Digital Twins can fully improve the yield and income of crop products and solve the problems of food security. In this paper, the development prospect of Digital Twins in agriculture is discussed.
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    GDot-i: Interactive System for Dot Paintings of Graphs
    (The Eurographics Association, 2022) Eades, Peter; Hong, Seok-Hee; McGrane, Martin; Meidiana, Amyra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    This poster presents GDot-i, an interactive system visualizing graphs and networks as dot paintings, inspired by the dot painting style of Central Australia. We describe the implementation of GDot-i, a web-based interactive system, including the user interface and typical use cases.
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    EuroVis 2022 Posters: Frontmatter
    (The Eurographics Association, 2022) Krone, Michael; Lenti, Simone; Schmidt, Johanna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
  • Item
    Automatic Segmentation of Tooth Images: Optimization of Multi-parameter Image Processing Workflow
    (The Eurographics Association, 2022) Bressan Fogalli, Giovani; Line, Sérgio Roberto Peres; Baum, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    The development of specific algorithms in image processing are usually related to dataset characteristics. Those characteristics will influence the number of instructions required to solve a problem. Normally, the more complex a set of instructions is, the more parameters need to be set. Dealing with such degrees of freedom, sometimes leading to subjective decision making, is time-consuming and frequently leads to errors or sub-optimal results of the developed model. Here, we deal with a model for segmentation of masks of tooth images containing a pattern of bands called Hunter-Schreger Bands (HSB). They appear on tooth surface when lit from the side. This segmentation process is only one step of a pipeline whose overall goal is human biometric identification to be used, e.g., in forensics. The segmentation algorithm, which exploits the anisotropy of the image, uses several parameters and choosing the optimal combination of them is challenging. The aim of this work was to utilize visual data analysis tools to optimize the chosen parameters and to understand their influence on the performance of the algorithm. Our results reveal that a slightly better combination of parameter values can be found starting from the experimentally determined initial parameters. This approach can be repeatedly performed to achieve even better parameterizations. To more deeply understand the influence of the parameters on the final result, more sophisticated visual interaction tools will be explored in future work.
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    Explorative Visual Analysis of Spatio-temporal Regions to Detect Hemodynamic Biomarker Candidates
    (The Eurographics Association, 2022) Derstroff, Adrian; Leistikow, Simon; Nahardani, Ali; Ebrahimi, Mahyasadat; Hoerr, Verena; Linsen, Lars; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Biomarkers are measurable biological properties that allow for distinguishing subjects of different cohorts such as healthy vs. diseased. In the context of diagnosing diseases of the cardiovascular system, researchers aim - among others - at detecting biomarkers in the form of spatio-temporal regions of blood flow obtained by medical imaging or of derived hemodynamical parameters. As the search space for such biomarkers in time-varying volumetric multi-field data is extremely large, we present an interactive visual exploration system to support the analysis of the potential of spatio-temporal regions to discriminate cohorts.
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    Visual Exploration of Genetic Sequence Variants in Pangenomes
    (The Eurographics Association, 2022) van den Brandt, Astrid; Jonkheer, Eef M.; van Workum, Dirk-Jan M.; Smit, Sandra; Vilanova, Anna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    To study the genetic sequence variation underlying traits of interest, the field of comparative genomics is moving away from analyses with single reference genomes to pangenomes; abstract representations of multiple genomes in a species or population. Pangenomes are beneficial because they represent a diverse set of genetic material and therefore avoid bias towards a single reference. While pangenomes allow for a complete map of the genetic variation, their large size and complex data structure hinder contextualization and interpretation of analysis results. Current visualization strategies fall short because they are created for single references or do not illustrate links to metadata. We present a work in progress version of a novel visual analytics strategy for pangenomic variant analysis. Our strategy is designed through an intensive involvement of genome scientists. The current design uniquely exploits interactive sorting, aggregation, and linkage relations from different perspectives of the data, to help the genome scientists explore and evaluate variant-trait associations in the context of multiple references and metadata.
  • Item
    Visualizing the Evolution of Multi-agent Game-playing Behaviors
    (The Eurographics Association, 2022) Agarwal, Shivam; Latif, Shahid; Rothweiler, Aristide; Beck, Fabian; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Analyzing the training evolution of AI agents in a multi-agent environment helps to understand changes in learned behaviors, as well as the sequence in which they are learned. We train an existing Pommerman team from scratch and, at regular intervals, let it battle against another top-performing team. We define thirteen game-specific behaviors and compute their occurrences in 600 matches. To investigate the evolution of these behaviors, we propose a visualization approach and showcase its usefulness in an application example.
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    Visually Explaining Publication Ranks in Citation-based Literature Search with PURE Suggest
    (The Eurographics Association, 2022) Beck, Fabian; Krause, Cedric; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Tracing citation links helps retrieve related publications. While most tools only allow the user to follow the citations of a single publication, some approaches support jointly analyzing the citations of a set of publications. Along similar lines, PURE suggest provides a detailed visual explanation of the ranking of suggested publications. The ranking is based on a score that combines citation numbers with keyword matching and is shown as a glyph for each publication. A citation network component references this glyph and visually embeds it into a timeline and cluster visualization.
  • Item
    A Design Space for Explainable Ranking and Ranking Models
    (The Eurographics Association, 2022) Hazwan, Ibrahim Al; Schmid, Jenny; Sachdeva, Madhav; Bernard, Jürgen; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Item ranking systems support users in multi-criteria decision-making tasks. Users need to trust rankings and ranking algorithms to reflect user preferences nicely while avoiding systematic errors and biases. However, today only few approaches help end users, model developers, and analysts to explain rankings. We report on the study of explanation approaches from the perspectives of recommender systems, explainable AI, and visualization research and propose the first cross-domain design space for explainers of item rankings. In addition, we leverage the descriptive power of the design space to characterize a) existing explainers and b) three main user groups involved in ranking explanation tasks. The generative power of the design space is a means for future designers and developers to create more target-oriented solutions in this only weakly exploited space.
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    Interactive Visualization of Machine Learning Model Results Predicting Infection Risk
    (The Eurographics Association, 2022) Schäfer, Steffen; Baumgartl, Tom; Wulff, Antje; Kuijper, Arjan; Marschollek, Michael; Scheithauer, Simone; von Landesberger, Tatiana; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    We present a novel visual-interactive interface to show results of a machine learning algorithm, which predicts the infection probability for patients in hospitals. The model result data is complex and needs to be presented in a clear and intuitive way to microbiology and infection control experts in hospitals. Our visual-interactive interface offers linked views which allow for detailed analysis of the model results. Feedback from microbiology and infection control experts showed that they were able to extract new insights regarding outbreaks and transmission pathways.
  • Item
    Visual Queries on Bipartite Multivariate Dynamic Social Networks
    (The Eurographics Association, 2022) Pister, Alexis; Prieur, Christophe; Fekete, Jean-Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    We present a new system for the visualization, visual query, and comparison of social networks modeled as bipartite multivariate dynamic graphs. Historians and sociologists study collections of documents -such as marriage acts, birth certificates, or contracts-from a region and time of interest and can transform them into Bipartite Multivariate Dynamic Social Networks to follow an in-depth analysis. However, few social network analysis tools are designed for this type of network, and existing ones rarely provide enough interactions to answer complex sociological questions.We present a new visual analytics system allowing to explore this type of network. In particular, we designed a visual query system specifically for this data model, allowing social scientists to easily apply filters on the topology and attributes of a network. We demonstrate how the interface coupled with the query system can be used to answer sociological questions with one real-world use case based on construction contracts.
  • Item
    MOBS - Multi-Omics Brush for Subgraph Visualisation
    (The Eurographics Association, 2022) Heylen, Dries; Peeters, Jannes; Ertaylan, Gökhan; Hooyberghs, Jef; Aerts, Jan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    One of the big opportunities in multi-omics analysis is the identification of interactions between molecular entities and their association with diseases. In analyzing and expressing these interactions in the search for new hypotheses, multi-omics data is often either translated into matrices containing pairwise correlations and distances, or visualized as node-link diagrams. A major problem when visualizing large networks however is the occurrence of hairball-like graphs, from which little to none information can be extracted. It is of interest to investigate subgroups of markers that are closely associated with each other, rather than just looking at the overload of all interactions. Hence, we propose MOBS (Multi-Omics Brush for Subgraph visualisation), a web-based visualisation interface that can provide both an overview and detailed views on the data. By means of a two dimensional brush on a heatmap that includes hierarchical clustering, relationships of interest can be extracted from a fully connected graph, to enable detailed analysis of the subgraph of interest.
  • Item
    A Mental Workload Estimation for Visualization Evaluation Using EEG Data and NASA-TLX
    (The Eurographics Association, 2022) Yim, Soobin; Yoon, Chanyoung; Yoo, Sangbong; Jang, Yun; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Mental workload is a cognitive effort felt by users while solving tasks, and good visualizations tend to induce a low mental workload. For better visualizations, various visualization techniques have been evaluated through quantitative methods that compare the response accuracy and performance time for completing visualization tasks. However, accuracy and time do not always represent the mental workload of a subject. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The electroencephalogram (EEG) as biosignal is one of the indicators frequently utilized to measure mental workload. Since everyone judges and senses differently, EEG signals and mental workload differ from person to person. In this paper, we propose a mental workload personalized estimation model with EEG data specialized for each individual to evaluate visualizations. We use scatter plot, bar, line, and map visualizations and collect NASA-TLX scores as mental workload and EEG data. NASA-TLX and EEG data as training data are used for the mental workload estimation model.
  • Item
    Validating Perception of Hyperspectral Textures in Virtual Reality Systems
    (The Eurographics Association, 2022) Díaz-Barrancas, Francisco; Cwierz, Halina; Gil-Rodríguez, Raquel; Pardo, Pedro J.; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Virtual reality (VR) environments are increasingly offering higher quality content. They use different computing techniques to improve the final user experience. In this work, we create different light sources and introduce hyperspectral textures for the object reflectance to boost the VR environment's quality. In addition, we perform a quantitative study to demonstrate that hyperspectral textures improve the final quality of the content in virtual reality systems.
  • Item
    Situated Visualization in Motion for Video Games
    (The Eurographics Association, 2022) Bucchieri, Federica; Yao, Lijie; Isenberg, Petra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    We contribute a systematic review of situated visualizations in motion in the context of video games. Video games produce rich dynamic datasets during gameplay that are often visualized to help players succeed in a game. Often these visualizations are moving either because they are attached to moving game elements or due to camera changes. We want to understand to what extent this motion and contextual game factors impact how players can read these visualizations. In order to ground our work, we surveyed 160 visualizations in motion and their embeddings in the game world. Here, we report on our analysis and categorization of these visualizations.
  • Item
    Using Data Comics to Enhance Visualization Literacy
    (The Eurographics Association, 2022) Boucher, Magdalena; Stoiber, Christina; Aigner, Wolfgang; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Visualization Literacy as a skill is becoming important, as growing amounts of data require complex ways of visualizing and interpreting them. Yet, it is hardly taught during general education, and not many resources conveying visualization knowledge in an easily accessible way exist. We draw on the notion of data comics, which are already well-suited for communicating visualization insights, but so far have not been explored in the context of teaching visualization skills. We aim to map the research landscape around this idea through a systematic literature research and present a first overview of related areas and how they might influence data comics used to enhance visualization literacy.
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    Accurate Molecular Atom Selection in VR
    (The Eurographics Association, 2022) Molina, Elena; Vázquez, Pere-Pau; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Accurate selection in cluttered scenes is complex because a high amount of precision is required. In Virtual Reality Environments, it is even worse, because it is more difficult for us to point a small object with our arms in the air. Not only our arms move slightly, but the button/trigger press reduces our weak stability. In this paper, we present two alternatives to the classical ray pointing intended to facilitate the selection of atoms in molecular environments. We have implemented and analyzed such techniques through an informal user study and found that they were highly appreciated by the users. This selection method could be interesting in other crowded environments beyond molecular visualization.
  • Item
    Visual Exploration of Preference-based Routes in Ski Resorts
    (The Eurographics Association, 2022) Rauscher, Julius; Miller, Matthias; Keim, Daniel A.; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Ski resorts exhibit a variety of available pistes and lifts, to which every skier has intrinsic preferences. While novices tend to favor easy pistes, experts might opt for more advanced pistes. In large resorts, the vast possibilities render manual, optimized routing according to specific piste and lift preferences extremely tedious. So far, existing visualizations of ski resorts lack these routing capabilities.We present a visual analytics interface that allows the user to find an optimal route between arbitrary locations in a ski resort according to individual personal preferences. Furthermore, we encode steepness information along the pistes to expose segments that deviate from the difficulty classification and thus are incompatible with the given user preferences.
  • Item
    Context Specific Visualizations on Smartwatches
    (The Eurographics Association, 2022) Islam, Alaul; Blascheck, Tanja; Isenberg, Petra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    We present an analysis of the results of a full-day context-specific ideation exercise for smartwatch visualizations. Participants of the exercise created 34 sketches during a sightseeing activity. Our analysis of these sketches showed where visualizations could be applied and shown, what information needs they could target, and how data could be represented in the sightseeing context.
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    Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems
    (The Eurographics Association, 2022) Benvenuti, Dario; Fiordeponti, Giovanni; Cheng, Hao; Catarci, Tiziana; Fekete, Jean-Daniel; Santucci, Giuseppe; Angelini, Marco; Battle, Leilani; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Designing big data visualization applications is challenging due to their complex yet isolated development. One of the most common issues is an increase in latency that can be experienced while interacting with the system. There exists a variety of optimization techniques to handle this issue in specific scenarios, but we lack models for integrating them in a holistic way, hindering the integration of complementary functionality and hampering consistent evaluation across systems. In response, we present a framework for modeling the big data visualization pipeline which builds a bridge between the Visualization, Human-Computer Interaction, and Database communities by integrating their individual contributions within a single, easily interpretable pipeline. With this framework, visualization applications can become aware of the full end-to-end context, making it easier to determine which subset of optimizations best suits the current context.
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    Visualizing Prediction Provenance in Regression Random Forests
    (The Eurographics Association, 2022) Médoc, Nicolas; Ciorna, Vasile; Petry, Frank; Ghoniem, Mohammad; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Random forest models are widely used in many application domains due to their performance and the fact that their constituent decision trees carry clear decision rules. Yet, the provenance of the predictions made by an entire forest is complex to grasp, which motivates application domain experts to adopt black-box testing strategies. We propose in this paper a coordinated multiple view system allowing to shed more light on prediction provenance, uncertainty and error in terms of bias and variance at the global model scale or at the local scale of decision paths and individual instances.
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    PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback
    (The Eurographics Association, 2022) Yu, Yuncong; Kruyff, Dylan; Jiao, Jiao; Becker, Tim; Behrisch, Michael; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome the uneconomic (re- )training with deep learning-based methods. Very high-dimensional time series emerge on an unprecedented scale due to increasing sensor usage and data storage. Visual pattern search is one of the most frequent tasks on such data. Automatic pattern retrieval methods often suffer from inefficient training, a lack of ground truth, and a discrepancy between the similarity perceived by the algorithm and the user. Our proposal is based on a query-aware locality-sensitive hashing technique to create a representation of multivariate time series windows. It features sub-linear training and inference time with respect to data dimensions. This performance gain allows an instantaneous relevance-feedback-driven adaption and converges to users' similarity notion. We are benchmarking PSEUDo in accuracy and speed with representative and state-of-the-art methods, evaluating its steerability through simulated user behavior, and designing expert studies to test PSEUDo's usability.
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    ANARI: ANAlytic Rendering Interface
    (The Eurographics Association, 2022) Griffin, Kevin; Amstutz, Jefferson; DeMarle, Dave; Günther, Johannes; Progsch, Jakob; Sherman, William; Stone, John E.; Usher, Will; Kooten, Kees van; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    The ANARI API enables users to build the description of a scene to generate imagery, rather than specifying the details of the rendering process, providing simplified visualization application development and cross-vendor portability to diverse rendering engines, including those using state-of-the-art ray tracing.
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    Visualizing Similarities between American Rap-Artists
    (The Eurographics Association, 2022) Meinecke, Christofer; Schebera, Jeremias; Eschrich, Jakob; Wiegreffe, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Rap music is one of the biggest music genres in the world today. Since the early days of rap music, references not only to pop culture but also to other rap artists have been an integral part of the lyrics' artistry. In addition, rap musicians reference each other by adopting fragments of lyrics, for example, to give credit. This kind of text reuse can be used to create connections between individual artists. Due to the large amount of lyrics, only automated detection methods can efficiently detect similarities between the songs and the artists. Here, we present a visualization system for analyzing rap music lyrics. We also trained a network tailored specifically for rap lyrics to compute similarities in lyrics. Here a video of the prototype can be seen.
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    Exploration and Analysis of Image-base Simulation Ensembles
    (The Eurographics Association, 2022) Dahshan, Mai; Turton, Terece L.; Polys, Nicholas; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Scientists run simulation ensembles to study the behavior of a phenomenon using varying initial conditions or input parameters. However, the I/O bottlenecks hinder performing large-scale multidimensional simulations. In situ visualization approaches address the variability of I/O performance by processing output data during simulation time and saving predetermined visualizations in image databases. This poster proposes a visual analytics approach to exploring and analyzing image-based simulation ensembles, taking advantage of semantic interaction, feature extraction, and deep learning techniques. Our approach uses deep learning and local feature techniques to learn image features and pass them along with the input parameters to the visualization pipeline for in-depth exploration and analysis of parameter and ensemble spaces simultaneously.
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    VisualBib(va): A Visual Analytics Platform for Authoring and Reviewing Bibliographies
    (The Eurographics Association, 2022) Dattolo, Antonina; Corbatto, Marco; Angelini, Marco; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms poorly support the visual exploration and comparative metadata analysis coming from subsequent searches. To address these issues, we designed and realized VisualBib(va), an online visual analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction, and metrics visualization for real-time scenarios. We introduce and discuss here the relevant functions that VisualBib(va) supports through one usage scenarios related to the creation of a bibliography.
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    Sustainable Urban Wastewater Treatment Visualizations
    (The Eurographics Association, 2022) Vega, Juan Marin; Uri-Carreño, Nerea; Kusnick, Jakob; Jänicke, Stefan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    The handling and treatment of urban wastewater are essential to protecting human health and the environment. However, its existence and importance are mostly invisible to the general public. In this work, we present a set of visualization system designed to communicate and visually explore the wastewater treatment system of Vandcenter Syd (VCS), one of the largest water utilities in Denmark on the island Fyn. It operates eight wastewater treatments, and our solution enables geographically exploring and comparing data collected in different facilities with an interactive map. We further provide a set of interactive visual interfaces to support exploring the energy consumption and production trends over the past 10 years. A case study on Ejby Mølle, VCS's largest facility, illustrates its transition from an energy consuming into an energy producing facility.
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    Visualization Challenges of Variant Interpretation in Multiscale NGS Data
    (The Eurographics Association, 2022) Ståhlbom, Emilia; Molin, Jesper; Lundström, Claes; Ynnerman, Anders; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    There is currently a movement in health care towards precision medicine, where genomics often is the central diagnostic component for tailoring the treatment to the individual patient. We here present results from a domain characterization effort to pinpoint problems and possibilities for visualization of genomics data in the clinical workflow, with analysis of copy number variants as an example task. Five distinct characteristics have been identified. Clinical genomics data is inherently multiscale, riddled with artifacts and uncertainty, and many findings have unknown significance, so it is a challenging visual analytics domain. Moreover, as in other clinical domains, high efficiency is key. This characterization will form the basis for follow-on visualization prototyping.
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    Interactive Attribution-based Explanations for Image Segmentation
    (The Eurographics Association, 2022) Humer, Christina; Elharty, Mohamed; Hinterreiter, Andreas; Streit, Marc; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Explanations of deep neural networks (DNNs) give users a better understanding of the inner workings and generalizability of a network. While the majority of research focuses on explanations for classification networks, in this work we focus on explainability for image segmentation networks. As a first contribution, we introduce a lightweight framework that allows generalizing certain attribution-based explanations, originally developed for classification networks, to also work for segmentation networks. The second contribution is a web-based tool that utilizes this framework and allows users to interactively explore segmentation networks. We demonstrate the approach using a self-trained mushroom segmentation network.
  • Item
    A Case Study on Implementing Screen Reader Accessibility in Dynamic Visualizations
    (The Eurographics Association, 2022) Costa, Rita; Malveiro, Beatriz; Palmeiro, João; Bizarro, Pedro; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Millions of people worldwide work in jobs where assessing dynamic data presented visually to them is a key part of their tasks. Since the data is only represented in a visual format, these occupations are out of reach for visually impaired people, making them unable to review hundreds of information-heavy cases per day and determine outcomes for each one in just a couple of minutes. In this work, we aim to shrink that gap by detailing the implementation of screen reader accessibility features to real-world visualizations used by fraud detection analysts. We propose a set of features that should be validated with users and, if proved to be useful, transformed into guidelines for creating these types of accessible charts.
  • Item
    On Visualizing Music Storage Media for Modern Access to Historic Sources
    (The Eurographics Association, 2022) Khulusi, Richard; Fricke, Heike; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Finding a balance between conserving historic objects and using them for research is one of the big issues in historic collections. Digitization holds the opportunity to offer a safe and non-destructible access to historic objects, making them available for research. With this poster, we want to give insight into our planned visualization system, using close and distant reading access for visual analysis approaches and allowing musicologists novel approaches to normally fragile and endangered media.
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    Enhancing Evaluation of Room Scale VR Studies to POI Visualizations in Minimaps
    (The Eurographics Association, 2022) Ajdadilish, Batoul; Kohl, Steffi; Schröder, Kay; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Understanding and evaluating user behavior in virtual reality environments is challenging for researchers. Stereoscopic perception is highly dependent on the point of view, so it is necessary to account for multiple spatial positions. Robust tools and methods to analyze these spatio-temporal data are lacking. We propose a design solution for spatio-temporal data visualization for room-scale VR studies. Our result is a top-down minimap that plots 3D point of interest coordinates of room-scale virtual reality environments to a 2D visualization. The video stream from the head mount display is next to the minimap showing the top-down view of the scene, reflecting the visual stimuli that were perceivable by the user. Both views are linked such that replaying the user session is synchronized in time. The minimap enables researchers to review and replay the recorded user session for in-depth study, allowing them to gain insightful information about users' behavior in virtual environments.
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    Chord2DS: An Extension to Chord Diagram to Show Data Elements from Two Heterogeneous Data Sources
    (The Eurographics Association, 2022) Humayoun, Shah Rukh; Brahmadevara, Likhitha; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    The standard Chord diagram, a radial layout, shows data elements in a circular fashion from one data source. In this paper, we propose an extension to the standard Chord diagram to show data elements from two heterogeneous data sources into one single diagram. The main Chord diagram is used for showing data elements and the relations between them from one data source, while we use an outer layer to show data elements from the second data source. The relationships between data elements from both data sources are shown through visual cues. The proposed solution uses space efficiently compared to using multiple diagrams in the scenarios of two heterogeneous data sources.
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    Parameter Sensitivity and Uncertainty Visualization in DTI
    (The Eurographics Association, 2022) Siddiqui, Faizan; Höllt, Thomas; Vilanova, Anna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
    Diffusion Tensor Imaging is a powerful technique that provides a unique insight into the complex structure of the brain's white matter. However, several sources of uncertainty limit its widespread use. Data and modeling errors arise due to acquisition noise and modeling transformations. Moreover, the sensitivities of the user-defined parameters and region definitions are not usually evaluated, a small change in these parameters can add large variations in the results. Without showing these uncertainties any visualization of DTI data can potentially be misleading. In our work, we develop a visual analytic tool that provides insight into the accumulated uncertainty in the visualization pipeline. The primary goal of this project is to develop an efficient visualization strategy that will assist the end-user in making critical decisions and make fiber tracking analysis less cumbersome and more reliable, a crucial step towards adoption in the neurosurgical workflow.