Selected Paper Awards & Personal Awards

For more awards, please browse our news section.

All Publications

  1. 2026

    1. M. Evers et al., “Uncertainty-Aware Visual Analysis of Force Networks in 2D Granular Materials,” Computer Graphics Forum, vol. 45, Art. no. 3, May 2026, doi: 10.1111/cgf.70438.
    2. R. Cutura, S. Sadler, Q. Q. Ngo, M. Aupetit, and M. Sedlmair, “ISilDR: Isometric Seriation-based Dimensionality Reduction for Visual Cluster Analysis,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, May 2026, doi: 10.1109/tvcg.2026.3694456.
    3. J. Satkunarajan, M. Abdelaal, S. Koch, K. Kurzhals, and D. Weiskopf, “Can LLMs Simulate Target Users in Visualization Case Studies?,” Computer Graphics Forum, vol. 45, Art. no. 3, May 2026, doi: 10.1111/cgf.70446.
    4. V. Oei et al., “RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo,” in 14th International Conference on Learning Representations (ICLR 2026), Apr. 2026.
    5. K. Angerbauer, S. König, M. Wieland, and M. Sedlmair, “Virtual Tours with Accessibility Annotations: A Prototype for Campus Accessibility Assessment for Mobility-Disabled Students,” in 2026 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE, Mar. 2026, pp. 1251–1252. doi: 10.1109/vrw70859.2026.00300.
    6. M. Wieland et al., “Making Eye Contact Accessible: Augmenting Gaze in Job Interviews for People with Visual Impairments,” in Proc. Augmented Humans International Conference, in AHs. ACM, Feb. 2026. doi: 10.1145/3795011.3795045.
    7. F. Miletić et al., “WoVis: Interactive Visualization of Word Embeddings for Semantic Change in Historical and Dialectal Language Resources,” in Proceedings of the Language Resources and Evaluation Conference, European Language Resources Association (ELRA), 2026, pp. 165–176. doi: 10.63317/3jzkx999kfxq.
    8. M. Koch, N. Pathmanathan, D. Weiskopf, and K. Kurzhals, “A Multimodal Framework for Understanding Collaborative Design Processes,” IEEE Transactions on Visualization and Computer Graphics, vol. 32, Art. no. 1, 2026, doi: 10.1109/tvcg.2025.3634232.
    9. C. Wu et al., “Probabilistic Inclusion Depth for Fuzzy Contour Ensemble Visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2026, doi: 10.1109/tvcg.2026.3694451.
    10. L. Joos, D. A. Keim, and M. T. Fischer, “Leveraging LLMs for semi-automatic corpus filtration in systematic literature reviews,” Computers & Graphics, vol. 135, p. 104537, 2026, doi: 10.1016/j.cag.2026.104537.

  2. 2025

    1. S. Hubenschmid et al., “SpatialMouse: A Hybrid Pointing Device for Seamless Interaction Across 2D and 3D Spaces,” in Proceedings of the 2025 31st ACM Symposium on Virtual Reality Software and Technology, New York, NY, USA: ACM, Nov. 2025, pp. 1–13. doi: 10.1145/3756884.3766047.
    2. M. Sönnichsen, M. Elfares, Y. Wang, R. Küsters, A. Roitberg, and A. Bulling, “AttentionLeak: What Does Human Attention Reveal About Information Visualisation?,” in International Conference on Document Analysis and Recognition, Cham: Springer Nature Switzerland, Sep. 2025, pp. 1–11. doi: 10.1007/978-3-032-04627-7_5.
    3. M. Chang, Y. Wang, H. W. Wang, A. Bulling, and C. X. Bearfield, “Grid Labeling: Crowdsourcing Task-Specific Importance from Visualizations,” in Proceedings of the Eurographics Conference on Visualization (EuroVis), ACM, Ed., Jun. 2025, pp. 1–6. doi: 10.1145/3715669.3725883.
    4. Z. Wu, Y. Wang, M. Langer, and A. M. Feit, “RelEYEance: Gaze-based Assessment of Users’ AI-reliance at Run-time,” in Proceedings of the ACM on Human-Computer Interaction, ACM, Ed., May 2025, pp. 1–18. doi: 10.1145/3725841.
    5. M. Evers and D. Weiskopf, “Uncertainty-Aware Spectral Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 31, Art. no. 10, 2025, doi: 10.1109/tvcg.2025.3542898.
    6. T. Krake, D. Klötzl, D. Hägele, and D. Weiskopf, “Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess,” IEEE Transactions on Visualization and Computer Graphics, vol. 31, Art. no. 2, 2025, doi: 10.1109/tvcg.2024.3364388.
    7. M. Jenadeleh et al., “Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity,” in International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2025. doi: 10.48550/arXiv.2506.12505.
    8. A. V. Reinschluessel et al., “Bridging Realities in a Heartbeat : How Integrating Heartbeat Signals Supports Collaboration in Mixed Reality,” in CHI Workshop on “Scaling Distributed Collaboration in Mixed Reality”, 2025. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-c76xaw7uu3xa8
    9. C. Flöter, S. Geringer, G. Reina, D. Weiskopf, and T. Ropinski, “Evaluating Foveated Frame Rate Reduction in Virtual Reality for Head-Mounted Displays,” in Proceedings of the 2025 Symposium on Eye Tracking Research and Applications, New York, NY, USA: ACM, 2025, pp. 1–7. doi: 10.1145/3715669.3725870.
    10. M. Testolina et al., “Fine-Grained Subjective Visual Quality Assessment for High-Fidelity Compressed Images,” in 2025 Data Compression Conference (DCC), IEEE, 2025, pp. 123–132. doi: 10.1109/dcc62719.2025.00020.
    11. C. Jiao, Y. Wang, G. Zhang, M. Bâce, Z. Hu, and A. Bulling, “DiffGaze: A Diffusion Model for Modelling Fine-grained Human Gaze Behaviour on 360° Images,” ACM Transactions on Interactive Intelligent Systems, pp. 1–22, 2025, doi: 10.1145/3772075.
    12. V. Hosu, L. Agnolucci, D. Iso, and D. Saupe, “Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and Resolution,” in International Conference on Computer Vision (ICCV), 2025. doi: 10.48550/arXiv.2502.06476.
    13. F. Schreiber et al., “Sustainable software development in science – insights from 20 years of Vanted,” Journal of Integrative Bioinformatics, p. 20250007, 2025, doi: 10.1515/jib-2025-0007.
    14. M. Stroh et al., “Using Saliency for Semantic Image Abstractions in Robotic Painting,” Computer Graphics Forum, vol. 44, Art. no. 7, 2025, doi: 10.1111/cgf.70259.
    15. R. Cutura et al., “SiGrid: Gridifying Scatterplots with Sector-Based Regularization and Hagrid,” in 2025 IEEE Visualization and Visual Analytics (VIS), IEEE, 2025, pp. 131–135. doi: 10.1109/vis60296.2025.00032.

  3. 2024

    1. S. A. Vriend, S. Vidyapu, K.-T. Chen, and D. Weiskopf, “Which Experimental Design is Better Suited for VQA Tasks?: Eye Tracking Study on Cognitive Load, Performance, and Gaze Allocations,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, ACM, Ed., New York, NY, USA: ACM, Jun. 2024, pp. 77:1–77:7. doi: 10.1145/3649902.3653519.
    2. Y. Wang et al., “SalChartQA: Question-driven Saliency on Information Visualisations,” in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI), ACM, May 2024, pp. 1–14. doi: 10.1145/3613904.3642942.
    3. L. Xiao et al., “A Systematic Review of Ability-diverse Collaboration through Ability-based Lens in HCI,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, New York, NY, USA: ACM, 2024, pp. 1–21. doi: 10.1145/3613904.3641930.
    4. M. M. Hamza, E. Ullah, A. Baggag, H. Bensmail, M. Sedlmair, and M. Aupetit, “ClustML: A measure of cluster pattern complexity in scatterplots learnt from human-labeled groupings,” Information Visualization, vol. 23, Art. no. 2, 2024, doi: 10.1177/14738716231220536.
    5. D. Blumberg, Y. Wang, A. Telea, D. A. Keim, and F. L. Dennig, “Inverting Multidimensional Scaling Projections Using Data Point Multilateration,” in Proceedings of the 15th International EuroVis Workshop on Visual Analytics (EuroVA), The Eurographics Association, 2024. doi: 10.2312/eurova.20241112.
    6. M. Jenadeleh et al., “An Image Quality Dataset with Triplet Comparisons for Multi-dimensional Scaling,” in 2024 16th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Ed., IEEE, 2024, pp. 278–281. doi: 10.1109/qomex61742.2024.10598258.
    7. G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair, and D. Weiskopf, “Scalability in Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, Art. no. 7, 2024, doi: 10.1109/tvcg.2022.3231230.
    8. J. Fuchs, F. L. Dennig, M.-V. Heinle, D. A. Keim, and S. Di Bartolomeo, “Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis,” Computer Graphics Forum, vol. 43, Art. no. 3, 2024, doi: 10.1111/cgf.15079.
    9. N. Kraus, M. Aichem, K. Klein, E. Lein, A. Jordan, and F. Schreiber, “TIBA: A web application for the visual analysis of temporal occurrences, interactions, and transitions of animal behavior,” PLOS Computational Biology, vol. 20, Art. no. 10, 2024, doi: 10.1371/journal.pcbi.1012425.
    10. D. Weiskopf, “Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in Light of Advanced AI,” in 2024 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), IEEE, Ed., IEEE, 2024, pp. 119–128. doi: 10.1109/beliv64461.2024.00019.
    11. L. Joos et al., “Evaluating Node Selection Techniques for Network Visualizations in Virtual Reality,” in ACM Symposium on Spatial User Interaction, New York, NY, USA: ACM, 2024, pp. 1–11. doi: 10.1145/3677386.3682102.
    12. V. Hosu, L. Agnolucci, O. Wiedemann, D. Iso, and D. Saupe, “UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment,” in Computer Vision – ECCV 2024 Workshops: Milan, Italy, September 29–October 4, 2024, Proceedings, Part IX., Cham: Springer Nature Switzerland, 2024, pp. 467–482. doi: 10.1007/978-3-031-91838-4_28.
    13. E. Scheurer, J. Schmalfuß, A. Lis, and A. Bruhn, “Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow,” in 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, 2024, pp. 6475–6484. doi: 10.1109/wacv57701.2024.00636.

  4. 2023

    1. F. Heyen, Q. Q. Ngo, and M. Sedlmair, “Visual Overviews for Sheet Music Structure,” in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023, ISMIR, Dec. 2023, pp. 692–699. doi: 10.5281/zenodo.10265383.
    2. J. Schmalfuß, L. Mehl, and A. Bruhn, “Distracting Downpour: Adversarial Weather Attacks for Motion Estimation.” IEEE, pp. 10072–10082, Oct. 2023. doi: 10.1109/iccv51070.2023.00927.
    3. E. Sood, L. Shi, M. Bortoletto, Y. Wang, P. Müller, and A. Bulling, “Improving Neural Saliency Prediction with a Cognitive Model of Human Visual Attention,” in Proceedings of the 45th Annual Meeting of the Cognitive Science Society (CogSci), Jul. 2023, pp. 3639–3646. [Online]. Available: https://escholarship.org/uc/item/5968p71m
    4. L. Mehl, J. Schmalfuß, A. Jahedi, Y. Nalivayko, and A. Bruhn, “Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2023, pp. 4981–4991. [Online]. Available: https://openaccess.thecvf.com/content/CVPR2023/html/Mehl_Spring_A_High-Resolution_High-Detail_Dataset_and_Benchmark_for_Scene_Flow_CVPR_2023_paper.html
    5. Y. Wang, M. Bâce, and A. Bulling, “Scanpath Prediction on Information Visualisations,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–15, Feb. 2023, doi: 10.1109/TVCG.2023.3242293.
    6. L. Mehl, A. Jahedi, J. Schmalfuß, and A. Bruhn, “M-FUSE: Multi-frame Fusion for Scene Flow Estimation.” IEEE, pp. 2019–2028, Jan. 2023. doi: 10.1109/wacv56688.2023.00206.
    7. M. Testolina, V. Hosu, M. Jenadeleh, D. Lazzarotto, D. Saupe, and T. Ebrahimi, “JPEG AIC-3 Dataset: Towards Defining the High Quality to Nearly Visually Lossless Quality Range.” IEEE, pp. 55–60, 2023. doi: 10.1109/qomex58391.2023.10178554.
    8. M. Jenadeleh, J. Zagermann, H. Reiterer, U.-D. Reips, R. Hamzaoui, and D. Saupe, “Relaxed forced choice improves performance of visual quality assessment methods.” IEEE, pp. 37–42, 2023. doi: 10.1109/qomex58391.2023.10178467.
    9. F. L. Dennig, M. Miller, D. A. Keim, and M. El-Assady, “FS/DS: A Theoretical Framework for the Dual Analysis of Feature Space and Data Space,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, Art. no. 8, 2023, doi: 10.1109/tvcg.2023.3288356.
    10. K.-T. Chen et al., “Reading Strategies for Graph Visualizations That Wrap Around in Torus Topology,” in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, in ETRA ’23. New York, NY, USA: Association for Computing Machinery, 2023. doi: 10.1145/3588015.3589841.
    11. N. Doerr, K. Angerbauer, M. Reinelt, and M. Sedlmair, “Bees, Birds and Butterflies: Investigating the Influence of Distractors on Visual Attention Guidance Techniques,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in CHI EA ’23. New York, NY, USA: Association for Computing Machinery, 2023. doi: 10.1145/3544549.3585816.
    12. M. Xue et al., “Taurus: Towards a Unified Force Representation and Universal Solver for Graph Layout,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209371.
    13. P. Fleck, A. Sousa Calepso, S. Hubenschmid, M. Sedlmair, and D. Schmalstieg, “RagRug: A Toolkit for Situated Analytics,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 7, 2023, doi: 10.1109/tvcg.2022.3157058.
    14. M. Koch, K. Kurzhals, M. Burch, and D. Weiskopf, “Visualization Psychology for Eye Tracking Evaluation,” in Visualization Psychology, D. Albers Szafir, R. Borgo, M. Chen, D. J. Edwards, B. Fisher, and L. Padilla, Eds., Cham: Springer International Publishing, 2023, pp. 243–260. doi: 10.1007/978-3-031-34738-2_10.
    15. T. Ge et al., “Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, Art. no. 8, 2023, doi: 10.1109/tvcg.2023.3284499.
    16. J. Schmalfuß, E. Scheurer, H. Zhao, N. Karantzas, A. Bruhn, and D. Labate, “Blind image inpainting with sparse directional filter dictionaries for lightweight CNNs,” Journal of Mathematical Imaging and Vision (JMIV), vol. 65, pp. 323–339, 2023, doi: 10.1007/s10851-022-01119-6.
    17. R. Bauer et al., “Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment,” Transport in Porous Media, 2023, doi: 10.1007/s11242-023-02019-y#citeas.
    18. C. Morariu, A. Bibal, R. Cutura, B. Frénay, and M. Sedlmair, “Predicting User Preferences of Dimensionality Reduction Embedding Quality,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, 2023, doi: 10.1109/tvcg.2022.3209449.
    19. V. Bruder, M. Larsen, T. Ertl, H. Childs, and S. Frey, “A Hybrid in Situ Approach for Cost Efficient Image Database Generation,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 9, 2023, doi: 10.1109/tvcg.2022.3169590.

  5. 2022

    1. K. Angerbauer and M. Sedlmair, “Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper).” IEEE, pp. 20–27, Oct. 2022. doi: 10.1109/beliv57783.2022.00007.
    2. A. Jahedi, L. Mehl, M. Rivinius, and A. Bruhn, “Multi-Scale Raft: Combining Hierarchical Concepts for Learning-Based Optical Flow Estimation,” in 2022 IEEE International Conference on Image Processing (ICIP), IEEE, Oct. 2022, pp. 1236–1240. doi: 10.1109/icip46576.2022.9898048.
    3. P. Schäfer, N. Rodrigues, D. Weiskopf, and S. Storandt, “Group Diagrams for Simplified Representation of Scanpaths,” in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI), ACM, Aug. 2022. doi: 10.1145/3554944.3554971.
    4. S. Dosdall, K. Angerbauer, L. Merino, M. Sedlmair, and D. Weiskopf, “Toward In-Situ Authoring of Situated Visualization with Chorded Keyboards,” in 15th International Symposium on Visual Information Communication and Interaction (VINCI), M. Burch, G. Wallner, and D. Limberger, Eds., ACM, Aug. 2022, pp. 1–5. doi: 10.1145/3554944.3554970.
    5. Y. Wang, M. Koch, M. Bâce, D. Weiskopf, and A. Bulling, “Impact of Gaze Uncertainty on AOIs in Information Visualisations,” in 2022 Symposium on Eye Tracking Research and Applications, ACM, Jun. 2022, pp. 1–6. doi: 10.1145/3517031.3531166.
    6. M. Koch, D. Weiskopf, and K. Kurzhals, “A Spiral into the Mind: Gaze Spiral Visualization for Mobile Eye Tracking,” Proceedings of the ACM on Computer Graphics and Interactive Techniques, vol. 5, Art. no. 2, May 2022, doi: 10.1145/3530795.
    7. G. Tkachev, R. Cutura, M. Sedlmair, S. Frey, and T. Ertl, “Metaphorical Visualization: Mapping Data to Familiar Concepts,” in CHI Conference on Human Factors in Computing Systems Extended Abstracts, ACM, Apr. 2022, pp. 1–10. doi: 10.1145/3491101.3516393.
    8. M. Philipp, N. Bacher, S. Sauer, F. Mathis-Ullrich, and A. Bruhn, “From Chairs To Brains: Customizing Optical Flow For Surgical Activity Localization.” IEEE, pp. 1–5, Mar. 2022. doi: 10.1109/isbi52829.2022.9761704.
    9. J. Görtler et al., “Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in CHI ’22. New York, NY, USA: Association for Computing Machinery, 2022, pp. 1–13. doi: 10.1145/3491102.3501823.
    10. H. Lin et al., “Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, Art. no. 9, 2022, doi: 10.1109/tcsvt.2022.3163860.
    11. D. Bienroth et al., “Spatially resolved transcriptomics in immersive environments,” Visual Computing for Industry, Biomedicine, and Art, vol. 5, Art. no. 1, 2022, doi: 10.1186/s42492-021-00098-6.
    12. J. Lou, H. Lin, D. Marshall, D. Saupe, and H. Liu, “TranSalNet: Towards perceptually relevant visual saliency prediction,” Neurocomputing, vol. 494, pp. 455–467, 2022, doi: 10.1016/j.neucom.2022.04.080.
    13. K. Angerbauer et al., “Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in CHI ’22. New York, NY, USA: Association for Computing Machinery, 2022. doi: 10.1145/3491102.3502133.
    14. D. Klötzl, T. Krake, Y. Zhou, I. Hotz, B. Wang, and D. Weiskopf, “Local bilinear computation of Jacobi sets,” The Visual Computer, vol. 38, Art. no. 9, 2022, doi: 10.1007/s00371-022-02557-4.
    15. T. Krake, M. von Scheven, J. Gade, M. Abdelaal, D. Weiskopf, and M. Bischoff, “Efficient Update of Redundancy Matrices for Truss and Frame Structures,” Journal of Theoretical, Computational and Applied Mechanics, 2022, doi: 10.46298/jtcam.9615.
    16. L. Joos, S. Jaeger-Honz, F. Schreiber, D. A. Keim, and K. Klein, “Visual Comparison of Networks in VR,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, Art. no. 11, 2022, doi: 10.1109/tvcg.2022.3203001.
    17. T. Krake, D. Klötzl, B. Eberhardt, and D. Weiskopf, “Constrained Dynamic Mode Decomposition,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2022, doi: 10.1109/tvcg.2022.3209437.
    18. D. Hägele, T. Krake, and D. Weiskopf, “Uncertainty-Aware Multidimensional Scaling,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, 2022, doi: 10.1109/TVCG.2022.3209420.
    19. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, and D. Weiskopf, “Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, 2022, doi: 10.1109/tvcg.2022.3209427.
    20. D. Hägele et al., “Uncertainty Visualization: Fundamentals and Recent Developments,” it - Information Technology, vol. 64, pp. 121–132, 2022, doi: 10.1515/itit-2022-0033.
    21. F. Chiossi et al., “Adapting visualizations and interfaces to the user,” it - Information Technology, vol. 64, pp. 133–143, 2022, doi: 10.1515/itit-2022-0035.
    22. T. Krake, A. Bruhn, B. Eberhardt, and D. Weiskopf, “Efficient and Robust Background Modeling with Dynamic Mode Decomposition,” Journal of Mathematical Imaging and Vision (2022), 2022, doi: 10.1007/s10851-022-01068-0.
    23. D. Weiskopf, “Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization,” Frontiers in Bioinformatics, vol. 2, 2022, doi: 10.3389/fbinf.2022.793819.
    24. S. Frey et al., “Parameter Adaptation In Situ: Design Impacts and Trade-Offs,” in In Situ Visualization for Computational Science, H. Childs, J. C. Bennett, and C. Garth, Eds., Cham: Springer International Publishing, 2022, pp. 159–182. doi: 10.1007/978-3-030-81627-8_8.
    25. K. Klein, M. Sedlmair, and F. Schreiber, “Immersive Analytics: An Overview,” it - Information Technology, vol. 64, pp. 155–168, 2022, doi: 10.1515/itit-2022-0037.
    26. Q. Q. Ngo, F. L. Dennig, D. A. Keim, and M. Sedlmair, “Machine Learning Meets Visualization – Experiences and Lessons Learned,” it - Information Technology, vol. 64, pp. 169–180, 2022, doi: 10.1515/itit-2022-0034.
    27. N. Brich et al., “Visual Analytics of Multivariate Intensive Care Time Series Data,” Computer Graphics Forum, vol. 41, Art. no. 6, 2022, doi: 10.1111/cgf.14498.
    28. F. Schreiber and D. Weiskopf, “Quantitative Visual Computing,” it - Information Technology, vol. 64, pp. 119–120, 2022, doi: 10.1515/itit-2022-0048.
    29. J. Schmalfuß, L. Mehl, and A. Bruhn, “Attacking Motion Estimation with Adversarial Snow,” in Proc. ECCV Workshop on Adversarial Robustness in the Real World (AROW), 2022. [Online]. Available: https://arxiv.org/abs/2210.11242
    30. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, and D. Weiskopf, “Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations.” DaRUS, 2022. [Online]. Available: https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3100
    31. N. Rodrigues, L. Shao, J. J. Yan, T. Schreck, and D. Weiskopf, “Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection,” in 2022 Symposium on Eye Tracking Research and Applications, in ETRA ’22. New York, NY, USA: Association for Computing Machinery, 2022, pp. 59:1–59:7. doi: 10.1145/3517031.3531165.
    32. H. Tarner, V. Bruder, T. Ertl, S. Frey, and F. Beck, “Visually Comparing Rendering Performance from Multiple Perspectives,” in Vision, Modeling, and Visualization, J. Bender, M. Botsch, and D. A. Keim, Eds., The Eurographics Association, 2022. doi: 10.2312/vmv.20221211.
    33. M. Becher et al., “Situated Visual Analysis and Live Monitoring for Manufacturing,” IEEE Computer Graphics and Applications, vol. 42, Art. no. 2, 2022, doi: 10.1109/mcg.2022.3157961.

  6. 2021

    1. C. Schulz et al., “Multi-Class Inverted Stippling,” ACM Trans. Graph., vol. 40, Art. no. 6, Dec. 2021, doi: 10.1145/3478513.3480534.
    2. K. Klein, D. Garkov, S. Rütschlin, T. Böttcher, and F. Schreiber, “QSDB—a graphical Quorum Sensing Database,” Database, vol. 2021, Art. no. 2021, Nov. 2021, doi: 10.1093/database/baab058.
    3. S. Hubenschmid, J. Zagermann, D. I. Fink, J. Wieland, T. Feuchtner, and H. Reiterer, “Towards Asynchronous Hybrid User Interfaces for Cross-Reality Interaction,” in ISS′21 Workshop Proceedings: “Transitional Interfaces in Mixed and Cross-Reality: A new frontier?”, H.-C. Jetter, J.-H. Schröder, J. Gugenheimer, M. Billinghurst, C. Anthes, M. Khamis, and T. Feuchtner, Eds., 2021. doi: 10.18148/kops/352-2-84mm0sggczq02.
    4. S. Su, V. Hosu, H. Lin, Y. Zhang, and D. Saupe, “KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects.” British Machine Vision Association, pp. 1–12, 2021. doi: 10.5244/c.35.249.
    5. F. Frieß, M. Becher, G. Reina, and T. Ertl, “Amortised Encoding for Large High-Resolution Displays.” IEEE, pp. 53–62, 2021. doi: 10.1109/ldav53230.2021.00013.
    6. J. Bernard, M. Hutter, M. Sedlmair, M. Zeppelzauer, and T. Munzner, “A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 11, pp. 1–42, 2021, doi: 10.1145/3439333.
    7. T. Krake, S. Reinhardt, M. Hlawatsch, B. Eberhardt, and D. Weiskopf, “Visualization and selection of Dynamic Mode Decomposition components for unsteady flow,” Visual Informatics, vol. 5, Art. no. 3, 2021, doi: 10.1016/j.visinf.2021.06.003.
    8. C. Morariu, A. Bibal, R. Cutura, B. Frénay, and M. Sedlmair, “DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality,” 2021. [Online]. Available: https://arxiv.org/abs/2105.09275
    9. L. Zhou, C. R. Johnson, and D. Weiskopf, “Data-Driven Space-Filling Curves,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030473.
    10. C. Bu et al., “SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, Art. no. 2, 2021, doi: 10.1109/tvcg.2020.3030404.
    11. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “ProSeCo: Visual analysis of class separation measures and dataset characteristics,” Computers & Graphics, vol. 96, pp. 48–60, 2021, doi: 10.1016/j.cag.2021.03.004.
    12. M. Burch, W. Huang, M. Wakefield, H. C. Purchase, D. Weiskopf, and J. Hua, “The State of the Art in Empirical User Evaluation of Graph Visualizations,” IEEE Access, vol. 9, pp. 4173–4198, 2021, doi: 10.1109/access.2020.3047616.
    13. N. Grossmann, J. Bernard, M. Sedlmair, and M. Waldner, “Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation,” in IEEE Visualization Conference (VIS, Short Paper), 2021, pp. 61–65. [Online]. Available: https://arxiv.org/abs/2110.07188
    14. K. Schatz et al., “2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution,” IEEE Computer Graphics and Applications, vol. 41, Art. no. 6, 2021, doi: 10.1109/MCG.2020.3004613.
    15. R. Cutura, K. Angerbauer, F. Heyen, N. Hube, and M. Sedlmair, “DaRt: Generative Art using Dimensionality Reduction Algorithms.” IEEE, pp. 59–72, 2021. doi: 10.1109/visap52981.2021.00013.
    16. M. M. Abbas, E. Ullah, A. Baggag, H. Bensmail, M. Sedlmair, and M. Aupetit, “ClustRank: A Visual Quality Measure Trained on Perceptual Data for Sorting Scatterplots by Cluster Patterns,” 2021. [Online]. Available: https://arxiv.org/pdf/2106.00599.pdf
    17. T. Müller, C. Schulz, and D. Weiskopf, “Adaptive polygon rendering for interactive visualization in the Schwarzschild spacetime,” European Journal of Physics, vol. 43, Art. no. 1, 2021, doi: 10.1088/1361-6404/ac2b36.
    18. C. Krauter, J. Vogelsang, A. Sousa Calepso, K. Angerbauer, and M. Sedlmair, “Don’t Catch It: An Interactive Virtual-Reality Environment to Learn About COVID-19 Measures Using Gamification Elements,” in Mensch und Computer, ACM, 2021, pp. 593–596. doi: 10.1145/3473856.3474031.
    19. R. Cutura, C. Morariu, Z. Cheng, Y. Wang, D. Weiskopf, and M. Sedlmair, “Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves,” in The 14th International Symposium on Visual Information Communication and Interaction, in VINCI 2021. New York, NY, USA: Association for Computing Machinery, 2021, p. 1:1—1:8. doi: 10.1145/3481549.3481569.

  7. 2020

    1. M. Blumenschein, “Pattern-Driven Design of Visualizations for High-Dimensional Data,” Konstanz, 2020. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-18wp9dhmhapww8
    2. M. Dias, D. Orellana, S. Vidal, L. Merino, and A. Bergel, “Evaluating a Visual Approach for Understanding JavaScript Source Code.” ACM, New York, NY, USA, pp. 128–138, Jul. 2020. doi: 10.1145/3387904.3389275.
    3. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “SepEx: Visual Analysis of Class Separation Measures,” in Proceedings of the International Workshop on Visual Analytics (EuroVA), C. Turkay and K. Vrotsou, Eds., The Eurographics Association, 2020, pp. 1–5. doi: 10.2312/eurova.20201079.
    4. A. Kumar, D. Mohanty, K. Kurzhals, F. Beck, D. Weiskopf, and K. Mueller, “Demo of the EyeSAC System for Visual Synchronization, Cleaning, and Annotation of Eye Movement Data,” in ACM Symposium on Eye Tracking Research and Applications, in ETRA ’20 Adjunct. New York, NY, USA: Association for Computing Machinery, 2020. doi: 10.1145/3379157.3391988.
    5. L. Zhou, M. Rivinius, C. R. Johnson, and D. Weiskopf, “Photographic High-Dynamic-Range Scalar Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, Art. no. 6, 2020, doi: 10.1109/TVCG.2020.2970522.
    6. N. Chotisarn et al., “A systematic literature review of modern software visualization,” Journal of Visualization, vol. 23, Art. no. 4, 2020, doi: 10.1007/s12650-020-00647-w.
    7. K. Kurzhals et al., “Visual Analytics and Annotation of Pervasive Eye Tracking Video,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), ACM, 2020, pp. 16:1–16:9. doi: 10.1145/3379155.3391326.
    8. X. Zhao, H. Lin, P. Guo, D. Saupe, and H. Liu, “Deep Learning VS. Traditional Algorithms for Saliency Prediction of Distorted Images.” IEEE, pp. 156–160, 2020. doi: 10.1109/icip40778.2020.9191203.
    9. N. Patkar, L. Merino, and O. Nierstrasz, “Towards Requirements Engineering with Immersive Augmented Reality,” in Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming, in ’20. New York, NY, USA: ACM, 2020, pp. 55–60. doi: 10.1145/3397537.3398472.
    10. M. Beck and S. Storandt, “Puzzling Grid Embeddings,” in Proceedings of the Symposium on Algorithm Engineering and Experiments, ALENEX 2020, Salt Lake City, UT, USA, January 5-6, 2020, 2020, pp. 94–105. doi: 10.1137/1.9781611976007.8.
    11. L. Merino, M. Schwarzl, M. Kraus, M. Sedlmair, D. Schmalstieg, and D. Weiskopf, “Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009-2019).” IEEE, pp. 438–451, 2020. doi: 10.1109/ismar50242.2020.00069.
    12. H. Men, V. Hosu, H. Lin, A. Bruhn, and D. Saupe, “Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database.” IEEE, pp. 1–6, 2020. doi: 10.1109/qomex48832.2020.9123096.
    13. S. Öney et al., “Evaluation of Gaze Depth Estimation from Eye Tracking in Augmented Reality,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Paper (ETRA-SP), ACM, 2020, pp. 49:1–49:5. doi: 10.1145/3379156.3391835.
    14. M. Blumenschein, L. J. Debbeler, N. C. Lages, B. Renner, D. A. Keim, and M. El-Assady, “v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions,” Computer Graphics Forum, vol. 39, Art. no. 3, 2020, doi: 10.1111/cgf14002.
    15. H. Men, V. Hosu, H. Lin, A. Bruhn, and D. Saupe, “Subjective annotation for a frame interpolation benchmark using artefact amplification,” Quality and User Experience, vol. 5, Art. no. 1, 2020, doi: 10.1007/s41233-020-00037-y.
    16. L. Merino et al., “Toward Agile Situated Visualization: An Exploratory User Study,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), 2020, pp. LBW087:1–LBW087:7. doi: 10.1145/3334480.3383017.
    17. K. Kurzhals, F. Göbel, K. Angerbauer, M. Sedlmair, and M. Raubal, “A View on the Viewer: Gaze-Adaptive Captions for Videos,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2020, pp. 139:1–139:12. doi: 10.1145/3313831.3376266.
    18. N. Pathmanathan et al., “Eye vs. Head: Comparing Gaze Methods for Interaction in Augmented Reality,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), ACM, 2020, pp. 50:1–50:5. doi: 10.1145/3379156.3391829.
    19. M. Kraus et al., “A Comparative Study of Orientation Support Tools in Virtual Reality Environments with Virtual Teleportation.” IEEE, pp. 227–238, 2020. doi: 10.1109/ismar50242.2020.00046.
    20. V. Hosu et al., “From Technical to Aesthetics Quality Assessment and Beyond: Challenges and Potential,” in Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, in ATQAM/MAST′20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 19–20. doi: 10.1145/3423268.3423589.

  8. 2019

    1. V. Bruder, K. Kurzhals, S. Frey, D. Weiskopf, and T. Ertl, “Space-Time Volume Visualization of Gaze and Stimulus,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), K. Krejtz and B. Sharif, Eds., ACM, 2019, pp. 12:1–12:9. doi: 10.1145/3314111.3319812.
    2. C. Fan et al., “SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning.” IEEE, pp. 1–6, 2019. doi: 10.1109/qomex.2019.8743204.
    3. H. Lin, V. Hosu, and D. Saupe, “KADID-10k: A Large-scale Artificially Distorted IQA Database.” IEEE, pp. 1–3, 2019. doi: 10.1109/qomex.2019.8743252.
    4. Y. Wang, Z. Wang, C.-W. Fu, H. Schmauder, O. Deussen, and D. Weiskopf, “Image-Based Aspect Ratio Selection,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, Art. no. 1, 2019, doi: 10.1109/tvcg.2018.2865266.
    5. T. M. Benz, B. Riedl, and L. L. Chuang, “Projection Displays Induce Less Simulator Sickness than Head-Mounted Displays in a Real Vehicle Driving Simulator,” in Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), C. P. Janssen, S. F. Donker, L. L. Chuang, and W. Ju, Eds., ACM, 2019, pp. 379–387. doi: 10.1145/3342197.3344515.
    6. T. Castermans, M. van Garderen, W. Meulemans, M. Nöllenburg, and X. Yuan, “Short Plane Supports for Spatial Hypergraphs,” in Graph Drawing and Network Visualization. GD 2018. Lecture Notes in Computer Science, vol. 11282, T. Biedl and A. Kerren, Eds., Springer International Publishing, 2019, pp. 53–66. doi: 10.1007/978-3-030-04414-5_4#citeas.
    7. C. Schulz et al., “A Framework for Pervasive Visual Deficiency Simulation.” IEEE, pp. 1–6, 2019. doi: 10.1109/vr44988.2019.9044164.
    8. R. Netzel, N. Rodrigues, A. Haug, and D. Weiskopf, “Compensation of Simultaneous Orientation Contrast in Superimposed Textures.” SCITEPRESS - Science and Technology Publications, pp. 48–57, 2019. doi: 10.5220/0007356800480057.
    9. M. Aupetit, M. Sedlmair, M. M. Abbas, A. Baggag, and H. Bensmail, “Toward Perception-Based Evaluation of Clustering Techniques for Visual Analytics.” IEEE, pp. 141–145, 2019. doi: 10.1109/visual.2019.8933620.
    10. C. Müller, M. Braun, and T. Ertl, “Optimised Molecular Graphics on the HoloLens,” in IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2019, Osaka, Japan, March 23-27, 2019, IEEE, 2019, pp. 97–102. doi: 10.1109/VR.2019.8798111.

  9. 2018

    1. S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “Employing Tangible Visualisations in Augmented Reality with Mobile Devices,” in Proceedings of the Working Conference on Advanced Visual Interfaces (AVI), 2018, pp. 1–4. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1iooenfo4fofm8
    2. S. Frey, “Spatio-Temporal Contours from Deep Volume Raycasting,” Computer Graphics Forum, vol. 37, Art. no. 3, 2018, doi: 10.1111/cgf.13438.
    3. C. Schulz, K. Schatz, M. Krone, M. Braun, T. Ertl, and D. Weiskopf, “Uncertainty Visualization for Secondary Structures of Proteins,” in 2018 IEEE Pacific Visualization Symposium (PacificVis), IEEE, 2018, pp. 96–105. doi: 10.1109/pacificvis.2018.00020.
    4. C. Glatz and L. L. Chuang, “The Time Course of Auditory Looming Cues in Redirecting Visuo-Spatial Attention,” Nature - Scientific Reports, vol. 9, pp. 743:1–743:10, 2018, doi: 10.1038/s41598-018-36033-8.
    5. D. Maurer, Y. C. Ju, M. Breuß, and A. Bruhn, “Combining Shape from Shading and Stereo: A Joint Variational Method for Estimating Depth, Illumination and Albedo,” International Journal of Computer Vision, vol. 126, Art. no. 12, 2018, doi: 10.1007/s11263-018-1079-1.
    6. V. Schwind, K. Leicht, S. Jäger, K. Wolf, and N. Henze, “Is there an Uncanny Valley of Virtual Animals? A Quantitative and Qualitative Investigation,” International Journal of Human-Computer Studies, vol. 111, pp. 49–61, 2018, doi: 10.1016/j.ijhcs.2017.11.003.
    7. V. Bruder, M. Hlawatsch, S. Frey, M. Burch, D. Weiskopf, and T. Ertl, “Volume-Based Large Dynamic Graph Analytics.” IEEE, pp. 210–219, 2018. doi: 10.1109/iv.2018.00045.
    8. N. Rodrigues and D. Weiskopf, “Nonlinear Dot Plots,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2744018.
    9. J. Görtler, C. Schulz, O. Deussen, and D. Weiskopf, “Bubble Treemaps for Uncertainty Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2743959.
    10. M. Jenadeleh, M. Pedersen, and D. Saupe, “Realtime Quality Assessment of Iris Biometrics Under Visible Light.” IEEE, pp. 556–55609, 2018. doi: 10.1109/cvprw.2018.00085.
    11. V. Hosu, H. Lin, and D. Saupe, “Expertise screening in crowdsourcing image quality,” in 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2018, pp. 1–6. doi: 10.1109/qomex.2018.8463427.

  10. 2017

    1. C. Schulz, A. Nocaj, J. Görtler, O. Deussen, U. Brandes, and D. Weiskopf, “Probabilistic Graph Layout for Uncertain Network Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598919.
    2. K. Kurzhals, M. Stoll, A. Bruhn, and D. Weiskopf, “FlowBrush: Optical Flow Art,” in Symposium on Computational Aesthetics, Sketch-Based Interfaces and Modeling, and Non-Photorealistic Animation and Rendering (EXPRESSIVE, co-located with SIGGRAPH)., 2017, pp. 1:1–1:9. doi: 10.1145/3092912.3092914.
    3. M. Heinemann, V. Bruder, S. Frey, and T. Ertl, “Power Efficiency of Volume Raycasting on Mobile Devices,” in Proceedings of the Eurographics Conference on Visualization (EuroVis) - Poster Track, E. Association, Ed., 2017. doi: 10.2312/eurp.20171166.
    4. M. Burch, M. Hlawatsch, and D. Weiskopf, “Visualizing a Sequence of a Thousand Graphs (or Even More),” Computer Graphics Forum, vol. 36, Art. no. 3, 2017, doi: 10.1111/cgf.13185.
    5. D. Sacha et al., “What You See Is What You Can Change: Human-Centered Machine Learning by Interactive Visualization,” Neurocomputing, vol. 268, pp. 164–175, 2017.
    6. L. Merino et al., “On the Impact of the Medium in the Effectiveness of 3D Software Visualizations,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), IEEE, 2017, pp. 11–21. [Online]. Available: https://ieeexplore.ieee.org/document/8091182
    7. J. Zagermann, U. Pfeil, C. Acevedo, and H. Reiterer, “Studying the Benefits and Challenges of Spatial Distribution and Physical Affordances in a Multi-device Workspace,” in Proceedings of the International Conference on Mobile and Ubiquitous Multimedia (MUM), 2017, pp. 249–259. doi: 10.1145/3152832.3152855.
    8. K. Kurzhals, M. Hlawatsch, C. Seeger, and D. Weiskopf, “Visual Analytics for Mobile Eye Tracking,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598695.
    9. V. Schwind, P. Knierim, C. Tasci, P. Franczak, N. Haas, and N. Henze, ““These are not my hands!”: Effect of Gender on the Perception of Avatar Hands in Virtual Reality,” Proceedings of the 2017 Conference on Human Factors in Computing Systems (CHI′17), pp. 1577–1582, 2017, doi: 10.1145/3025453.3025602.
    10. D. Maurer, M. Stoll, and A. Bruhn, “Order-adaptive Regularisation for Variational Optical Flow: Global, Local and in Between.,” in Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science, F. Lauze, Y. Dong, and A. B. Dahl, Eds., Springer International Publishing, 2017, pp. 550–562. doi: 10.1007/978-3-319-58771-4_44.
    11. M. A. Baazizi, H. Ben Lahmar, D. Colazzo, G. Ghelli, and C. Sartiani, “Schema Inference for Massive JSON Datasets,” in Proceedings of the Conference on Extending Database Technology (EDBT), 2017, pp. 222–233. doi: 10.5441/002/edbt.2017.21.
    12. D. Sacha et al., “Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, doi: 10.1109/tvcg.2016.2598495.
    13. V. Hosu et al., “The Konstanz natural video database (KoNViD-1k).,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2017, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/document/7965673
    14. D. Jäckle, F. Stoffel, S. Mittelstädt, D. A. Keim, and H. Reiterer, “Interpretation of Dimensionally-Reduced Crime Data: A Study with Untrained Domain Experts,” in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 2017, pp. 164–175. [Online]. Available: https://bib.dbvis.de/publications/details/697
    15. N. Rodrigues, M. Burch, L. Di Silvestro, and D. Weiskopf, “A Visual Analytics Approach for Word Relevances in Multiple Texts,” in Proceedings of the International Conference on Information Visualisation (IV), IEEE, 2017, pp. 1–7. [Online]. Available: https://ieeexplore.ieee.org/document/8107940
    16. P. Gralka, C. Schulz, G. Reina, D. Weiskopf, and T. Ertl, “Visual Exploration of Memory Traces and Call Stacks,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), IEEE, 2017, pp. 54–63. doi: 10.1109/VISSOFT.2017.15.
    17. C. Schulz, M. Burch, F. Beck, and D. Weiskopf, “Visual Data Cleansing of Low-Level Eye Tracking Data,” in Eye Tracking and Visualization: Foundations, Techniques, and Applications. ETVIS 2015, M. Burch, L. L. Chuang, B. Fisher, A. Schmidt, and D. Weiskopf, Eds., Springer International Publishing, 2017, pp. 199–216. doi: 10.1007/978-3-319-47024-5_12.
    18. A. Nesti, K. de Winkel, and H. H. Bülthoff, “Accumulation of Inertial Sensory Information in the Perception of Whole Body Yaw Rotation,” PloS ONE, vol. 12, Art. no. 1, 2017, doi: 10.1371/journal.pone.0170497.
    19. D. Bahrdt et al., “Growing Balls in ℝd,” in Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX), S. P. Fekete and V. Ramachandran, Eds., SIAM, 2017, pp. 247–258. doi: 10.1137/1.9781611974768.20.
    20. R. Netzel, M. Hlawatsch, M. Burch, S. Balakrishnan, H. Schmauder, and D. Weiskopf, “An Evaluation of Visual Search Support in Maps,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598898.
    21. R. Netzel, J. Vuong, U. Engelke, S. I. O’Donoghue, D. Weiskopf, and J. Heinrich, “Comparative Eye-tracking Evaluation of Scatterplots and Parallel Coordinates,” Visual Informatics, vol. 1, Art. no. 2, 2017, doi: 10.1016/j.visinf.2017.11.001.
    22. M. Stoll, D. Maurer, and A. Bruhn, “Variational Large Displacement Optical Flow Without Feature Matches.,” in Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science, M. Pelillo and E. R. Hancock, Eds., Springer International Publishing, 2017, pp. 79–92. doi: 10.1007/978-3-319-78199-0_6.
    23. U. Gadiraju et al., “Crowdsourcing Versus the Laboratory: Towards Human-Centered Experiments Using the Crowd,” in Lecture Notes in Computer Science, D. Archambault, H. Purchase, and T. Hossfeld, Eds., in Information Systems and Applications, incl. Internet/Web, and HCI. , Cham: Springer International Publishing, 2017, pp. 6–26. doi: 10.1007/978-3-319-66435-4_2.
    24. M. Stein et al., “Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2017, doi: 10.1109/tvcg.2017.2745181.

  11. 2016

    1. R. Netzel, M. Burch, and D. Weiskopf, “User Performance and Reading Strategies for Metro Maps: An Eye Tracking Study,” Special Issue on Eye Tracking for Spatial Research in Spatial Cognition and Computation: An Interdisciplinary Journal, 2016, doi: 10.1080/13875868.2016.1226839.
    2. R. Netzel and D. Weiskopf, “Hilbert Attention Maps for Visualizing Spatiotemporal Gaze Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), 2016, pp. 21–25. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7851160
    3. P. Xu, Y. Sugano, and A. Bulling, “Spatio-Temporal Modeling and Prediction of Visual Attention in Graphical User Interfaces,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2016, pp. 3299–3310.
    4. P. Tutzauer, S. Becker, D. Fritsch, T. Niese, and O. Deussen, “A Study of the Human Comprehension of Building Categories Based on Different 3D Building Representations,” Photogrammetrie - Fernerkundung - Geoinformation, vol. 2016, pp. 319–333, 2016, doi: 10.1127/pfg/2016/0302.
    5. D. Sacha et al., “Human-Centered Machine Learning Through Interactive Visualization: Review and Open Challenges.,” in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2016. [Online]. Available: http://dblp.uni-trier.de/db/conf/esann/esann2016.html#SachaSZLWNK16
    6. A. Kumar, R. Netzel, M. Burch, D. Weiskopf, and K. Mueller, “Multi-Similarity Matrices of Eye Movement Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), 2016, pp. 26–30. [Online]. Available: https://ieeexplore.ieee.org/document/7851161
    7. M. Burch, R. Woods, R. Netzel, and D. Weiskopf, “The Challenges of Designing Metro Maps,” Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016, doi: 10.5220/0005679601950202.
    8. T. Blascheck, F. Beck, S. Baltes, T. Ertl, and D. Weiskopf, “Visual analysis and coding of data-rich user behavior,” in IEEE Conference on Visual Analytics Science and Technology, IEEE, 2016, pp. 141–150. doi: 10.1109/vast.2016.7883520.
    9. M. Hund et al., “Visual Analytics for Concept Exploration in Subspaces of Patient Groups,” Brain Informatics, vol. 3, Art. no. 4, 2016, doi: 10.1007/s40708-016-0043-5.
    10. A. Barth and F. G. Fuchs, “Uncertainty Quantification for Hyperbolic Conservation Laws with Flux Coefficients Given by Spatiotemporal Random Fields,” SIAM Journal on Scientific Computing, vol. 38, Art. no. 4, 2016, doi: 10.1137/15M1027723.
    11. A. Nocaj, M. Ortmann, and U. Brandes, “Adaptive Disentanglement Based on Local Clustering in Small-World Network Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, Art. no. 6, 2016, [Online]. Available: http://dblp.uni-trier.de/db/journals/tvcg/tvcg22.html#NocajOB16
    12. L. Lischke, V. Schwind, K. Friedrich, A. Schmidt, and N. Henze, “MAGIC-Pointing on Large High-Resolution Displays,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), ACM, Ed., ACM, 2016, pp. 1706–1712. doi: 10.1145/2851581.2892479.
    13. V. Schwind and S. Jäger, “The Uncanny Valley and the Importance of Eye Contact,” in Mensch und Computer 2015 - Tagungsband, Oldenbourg Wissenschaftsverlag, 2016, pp. 153–162. doi: 10.1515/icom-2016-0001.
    14. A. Voit, T. Machulla, D. Weber, V. Schwind, S. Schneegaß, and N. Henze, “Exploring Notifications in Smart Home Environments,” in Proceedings of the International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI), ACM, Ed., 2016, pp. 942–947. doi: 10.1145/2957265.2962661.
    15. D. Weiskopf, M. Burch, L. L. Chuang, B. Fischer, and A. Schmidt, Eye Tracking and Visualization: Foundations, Techniques, and Applications. Berlin, Heidelberg: Springer, 2016. [Online]. Available: https://www.springer.com/de/book/9783319470238
    16. K. Kurzhals, B. Fisher, M. Burch, and D. Weiskopf, “Eye Tracking Evaluation of Visual Analytics,” Information Visualization, vol. 15, Art. no. 4, 2016, doi: 10.1177/1473871615609787.

  12. 2015

    1. L. L. Chuang and H. H. Bülthoff, “Towards a Better Understanding of Gaze Behavior in the Automobile,” in Position papers of the workshops at AutomotiveUI′15. Sep. 2015. [Online]. Available: https://www.auto-ui.org/15/p/workshops/2/8_Towards%20a%20Better%20Understanding%20of%20Gaze%20Behavior%20in%20the%20Automobile_Chuang.pdf
    2. L. Lischke, J. Grüninger, K. Klouche, A. Schmidt, P. Slusallek, and G. Jacucci, “Interaction Techniques for Wall-Sized Screens,” Proceedings of the International Conference on Interactive Tabletops & Surfaces (ITS), pp. 501–504, 2015, doi: 10.1145/2817721.2835071.
    3. C. Schulz, M. Burch, and D. Weiskopf, “Visual Data Cleansing of Eye Tracking Data,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), 2015. [Online]. Available: http://etvis.visus.uni-stuttgart.de/etvis2015/papers/etvis15_schulz.pdf
    4. M. Spicker, J. Kratt, D. Arellano, and O. Deussen, “Depth-aware Coherent Line Drawings,” in Proceedings of the SIGGRAPH Asia Symposium on Computer Graphics and Interactive Techniques, Technical Briefs, ACM, 2015, pp. 1:1–1:5. doi: 10.1145/2820903.2820909.

Project Group A

Models and Measures

A01 | Uncertainty Quantification and Analysis
A03 | Quantification of Visual Explainability
A07 | Visual Attention Modeling
A08 | Learning and Explaining Dimensionality Reduction
A09 | Scalability and Complexity in Immersive Analytics

 

Completed

A02 | Quantifying Visual Computing Systems
A04 | Quantitative Models for Visual Abstraction
A05 | Visual Quality Assessment
A06 | Computational Photography

 

Project Group B

Adaptive Algorithms

B01 | Adaptive Self-Consistent Visualization
B04 | Motion Estimation
B06 | Adaptive Algorithms for Graph Views and View Transitions

 

Completed

B02 | Adaptive Network Visualization
B05 | Large Scale Variational 3D Reconstruction
B07 | Computational Uncertainty Quantification

 

Project Group C

Interaction

C01 | Evaluating Mixed Reality Experiences
C05 | Human-Machine Interaction with Adaptive Multisensory Systems
C06 | User-Adaptive Mixed Reality
C07 | Optimization for Dynamic XR User Interfaces

 

Completed

C02 | Physiologically Based Interaction
C03 | Immersive Virtual Environments
C04 | Mobile Visualization and Interaction
Quantifying interactions with adaptable multisensory systems

 

Project Group D

Applications

D02 | Visual Analytics in Linguistics
D04 | Immersive Analytics for the Life Sciences
D05 | Visual Computing for ERP-based Brain Activity

 

Completed

D01 | Modeling of 3D Virtual Cities
D03 | Exploration and Analysis of Provenance Data

 

Central Services

INF | Collaboration Infrastructure
Ö | Public Relations

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