Selected Paper Awards & Personal Awards

For more awards, please browse our news section.

All Publications

  1. 2025

    1. S. Hubenschmid et al., “Revisiting Hybrid Input Devices for Immersive Analytics,” in Human Factors in Immersive Analytics Workshop at IEEE VIS 2025, Vienna, Nov. 2025.
    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. T. Nishiyasu, T. Kostorz, Y. Wang, Y. Sato, and A. Bulling, “ChartQC: Question Classification from Human Attention Data on Charts,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), May 2025, pp. 1–6. doi: 10.1145/3715669.3725883.
    5. 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.
    6. D. Shi, Y. Wang, Y. Bai, A. Bulling, and A. Oulasvirta, “Chartist: Task-driven Eye Movement Control for Chart Reading,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, New York, NY, United States, May 2025, pp. 1–14. doi: 10.1145/3706598.3713128.
    7. R. Bauer, M. Evers, Q. Q. Ngo, G. Reina, S. Frey, and M. Sedlmair, “Voronoi Cell Interface‐Based Parameter Sensitivity Analysis for Labeled Samples,” Computer Graphics Forum, May 2025, doi: 10.1111/cgf.70122.
    8. M. Koch et al., “Group Gaze-Sharing with Projection Displays,” in Proceedings of the 2025 Symposium on Eye Tracking Research and Applications, New York, NY, USA: ACM, 2025, pp. 1–7. doi: 10.1145/3715669.3725871.
    9. 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.
    10. 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
    11. R. Jianu, N. Silva, N. Rodrigues, T. Blascheck, T. Schreck, and D. Weiskopf, “Gaze‐Aware Visualisation: Design Considerations and Research Agenda,” Computer Graphics Forum, 2025, doi: 10.1111/cgf.70097.
    12. L. Zhang et al., “Towards a Better Understanding of Graph Perception in Immersive Environments,” in 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025), 2025, pp. 1–19. doi: 10.4230/LIPIcs.GD.2025.9.
    13. D. Saupe and S. H. Del Pin, “Uncovering Cultural Influences on Perceptual Image and Video Quality Assessment through Adaptive Quantized Metric Models,” Journal of Perceptual Imaging, vol. 8, Art. no. 0, 2025, doi: 10.2352/j.percept.imaging.2025.7.000407.
    14. 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.
    15. M.-M. Zymla, K. Kruschwitz, and P. Zodl, “An instructive implementation of semantic parsing and reasoning using Lexical Functional Grammar,” in Proceedings of the 2nd Bridging the Gap between Human and Automated Reasoning Workshop (BriGap-2), 2025.
    16. 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.
    17. D. Garkov et al., “Interactive delineation and quantification of anatomical structure with virtual reality,” bioRxiv 2025.06.17.659041, 2025, doi: 10.1101/2025.06.17.659041.
    18. S. Geringer et al., “Eyes in the Clouds: Spatial Data Analysis with Gaze-Enhanced Point Clouds,” in 2nd Japan Visualization Symposium (JapanVis 2025), non-archival event, 2025.
    19. D. Bienroth et al., “Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics,” Genome Biology, vol. 26, Art. no. 1, 2025, doi: 10.1186/s13059-025-03630-6.
    20. L. Joos, D. A. Keim, and M. T. Fischer, “Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews,” in EuroVis Workshop on Visual Analytics (EuroVA), 2025. doi: 10.2312/eurova.20251105.
    21. 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.
    22. D. Saupe and T. Bleile, “Robustness and Accuracy of MOS with Hard and Soft Outlier Detection,” in International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2025.
    23. 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.
    24. L. Joos et al., “Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings,” in 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025), 2025. doi: 10.4230/LIPIcs.GD.2025.35.
    25. 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.
    26. P. Gralka, C. Müller, S. Geringer, G. Reina, and D. Weiskopf, “Quantifying Energy Reduction of Foveated Volume Visualization,” in Proceedings of the 2025 Symposium on Eye Tracking Research and Applications, New York, NY, USA: ACM, 2025, pp. 1–7. doi: 10.1145/3715669.3725881.
    27. N. Gröne et al., “Interweaving Mathematics and Art: Drawing Graphs as Celtic Knots and Links with CelticGraph,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–12, 2025, doi: 10.1109/tvcg.2025.3545481.
    28. M. Jenadeleh, J. Sneyers, P. Jia, S. Mohammadi, J. Ascenso, and D. Saupe, “Subjective Visual Quality Assessment for High-Fidelity Learning-Based Image Compression,” in International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2025. doi: 10.48550/arXiv.2504.06301.
    29. S. Mohammadi et al., “In-place Double Stimulus Methodology for Subjective Assessment of High Quality Images,” in European Workshop on Visual Information Processing (EUVIP), 2025. doi: 10.48550/arXiv.2508.09777.
  2. 2024

    1. J. Wieland, H. Cho, S. Hubenschmid, A. Kiuchi, H. Reiterer, and D. Lindlbauer, “Push2AR: Enhancing Mobile List Interactions Using Augmented Reality,” in 2024 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), IEEE, Oct. 2024, pp. 671–680. doi: 10.1109/ismar62088.2024.00082.
    2. P. Gralka, C. Müller, M. Heinemann, G. Reina, D. Weiskopf, and T. Ertl, “Power Overwhelming: The One With the Oscilloscopes,” Journal of Visualization, Aug. 2024, doi: 10.1007/s12650-024-01001-0.
    3. Y. Wang, Y. Jiang, Z. Hu, C. Ruhdorfer, M. Bâce, and A. Bulling, “VisRecall++: Analysing and Predicting Visualisation Recallability from Gaze Behaviour,” Proc. ACM on Human-Computer Interaction (PACM HCI), vol. 8, pp. 1–18, Jul. 2024, doi: 10.1145/3655613.
    4. 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 Symposium on Eye Tracking and Visualization (ETVIS), Jun. 2024. [Online]. Available: https://arxiv.org/abs/2404.04036
    5. D. Saupe and S. Hviid del Pin, “National differences in image quality assessment: An investigation on three large-scale IQA datasets,” in 2024 16th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Ed., IEEE, May 2024, pp. 214–220. doi: 10.1109/qomex61742.2024.10598250.
    6. M. Jenadeleh, A. Heß, S. Hviid del Pin, E. Gamboa, M. Hirth, and D. Saupe, “Impact of feedback on crowdsourced visual quality assessment with paired comparisons,” in 2024 16th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Ed., IEEE, May 2024, pp. 125–131. doi: 10.1109/qomex61742.2024.10598256.
    7. M. Jenadeleh, R. Hamzaoui, U.-D. Reips, and D. Saupe, “Crowdsourced Estimation of Collective Just Noticeable Difference for Compressed Video with the Flicker Test and QUEST+,” IEEE Transactions on Circuits and Systems for Video Technology, p. 1, May 2024, doi: 10.1109/tcsvt.2024.3402363.
    8. 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.
    9. M. Kurzweg, Y. Weiss, M. O. Ernst, A. Schmidt, and K. Wolf, “Survey on Haptic Feedback through Sensory Illusions in Interactive Systems,” ACM Comput. Surv., vol. 56, Art. no. 8, Apr. 2024, doi: 10.1145/3648353.
    10. Y. Xue et al., “Reducing Ambiguities in Line-Based Density Plots by Image-Space Colorization,” IEEE Transactions on Visualization & Computer Graphics, vol. 30, Art. no. 1, Jan. 2024, [Online]. Available: https://www.computer.org/csdl/journal/tg/2024/01/10297597/1RyY1MBMcIo
    11. N. Gröne, B. Grüneisen, K. Klein, B. de Bono, T. Czauderna, and F. Schreiber, “Layout of anatomical structures and blood vessels based on the foundational model of anatomy,” Journal of Integrative Bioinformatics, vol. 21, Art. no. 3, 2024, doi: 10.1515/jib-2024-0023.
    12. 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.
    13. F. L. Dennig et al., “The Categorical Data Map: A Multidimensional Scaling-Based Approach,” in 2024 IEEE Visualization in Data Science (VDS), IEEE, 2024, pp. 25–34. doi: 10.1109/vds63897.2024.00008.
    14. 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.
    15. C. Müller and T. Ertl, “Quantifying Performance Gains of DirectStorage for the Visualisation of Time-Dependent Particle Data Sets,” Journal of Visualization, 2024, doi: 10.1007/s12650-024-01036-3.
    16. M. Becher, C. Müller, D. Sellenthin, T. Ertl, G. Reina, and D. Weiskopf, “Your Visualisations are Going Places: SciVis on Gaming Consoles,” in Proc. JapanVis, 2024.
    17. 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.
    18. 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.
    19. V. Mikheev, R. Skukies, and B. Ehinger, “The Art of Brainwaves: A Survey on Event-Related Potential Visualization Practices,” Aperture Neuro, vol. 4, 2024, doi: 10.52294/001c.116386.
    20. S. Su et al., “Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model,” IEEE Transactions on Multimedia, vol. 26, pp. 2671–2685, 2024, doi: 10.1109/tmm.2023.3301276.
    21. D. Klötzl, T. Krake, M. Becher, M. Koch, D. Weiskopf, and K. Kurzhals, “NMF-Based Analysis of Mobile Eye-Tracking Data,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications, 2024, pp. 1–9. doi: 10.1145/3649902.3653518.
    22. S. P. Feyer, B. Pinaud, K. Klein, E. Lein, and F. Schreiber, “Exploring animal behaviour multilayer networks in immersive environments – a conceptual framework,” Journal of Integrative Bioinformatics, vol. 21, Art. no. 3, 2024, doi: 10.1515/jib-2024-0022.
    23. D. Garkov et al., “Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis,” arXiv preprint, 2024, doi: 10.48550/arXiv.2412.14776.
    24. K. Angerbauer et al., “Is it Part of Me? Exploring Experiences of Inclusive Avatar Use For Visible and Invisible Disabilities in Social VR,” in The 26th International ACM SIGACCESS Conference on Computers and Accessibility, New York, NY, USA: ACM, 2024, pp. 1–15. doi: 10.1145/3663548.3675601.
    25. Y. Zhang, H. Williams, F. Schreiber, and K. Klein, “Visualising the Invisible : Exploring Approaches for Visual Analysis of Dynamic Airflow in Geographic Environments Using Sensor Data,” in Proceedings of the EuroVis Workshop on Visual Analytics 2024, Eindhoven, 2024. doi: 10.2312/eurova.20241117.
    26. M. Koch, N. Pathmanathan, D. Weiskopf, and K. Kurzhals, “How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis,” in Proceedings of the 2024 Symposium on Eye Tracking Research and Applications (ETRA ’24), New York, NY, USA: ACM, 2024, pp. 1–7. doi: 10.1145/3649902.3653349.
    27. R. Buchmüller, B. Jäckl, M. Behrisch, D. A. Keim, and F. L. Dennig, “cPro: Circular Projections Using Gradient Descent,” in Proceedings of the 15th International EuroVis Workshop on Visual Analytics (EuroVA), The Eurographics Association, 2024. doi: 10.2312/eurova.20241111.
    28. 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.
    29. 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.
    30. J. Wieland et al., “Investigating the Potential of Haptic Props for 3D Object Manipulation in Handheld AR,” IEEE Transactions on Visualization and Computer Graphics, vol. 31, Art. no. 9, 2024, doi: 10.1109/tvcg.2024.3495021.
    31. D. I. Fink, M. Skowronski, J. Zagermann, A. V. Reinschluessel, H. Reiterer, and T. Feuchtner, “There Is More to Avatars Than Visuals: Investigating Combinations of Visual and Auditory User Representations for Remote Collaboration in Augmented Reality,” in Proceedings of the ACM on Human-Computer Interaction, Association for Computing Machinery (ACM), 2024, pp. 540–568. doi: 10.1145/3698148.
    32. L. Joos, B. Jäckl, D. A. Keim, M. T. Fischer, L. Peska, and J. Lokoč, “Known-Item Search in Video: An Eye Tracking-Based Study,” in Proceedings of the 2024 International Conference on Multimedia Retrieval (ICMR ’24), New York, NY, USA: ACM, 2024, pp. 311–319. doi: 10.1145/3652583.3658119.
    33. 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.
    34. P. Paetzold, D. Hägele, M. Evers, D. Weiskopf, and O. Deussen, “UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox.” IEEE, pp. 48–50, 2024. doi: 10.1109/uncertaintyvisualization63963.2024.00011.
    35. P. Eades et al., “CelticGraph: Drawing Graphs as Celtic Knots and Links,” in Graph Drawing and Network Visualization, M. A. Bekos and M. Chimani, Eds., Cham: Springer Nature Switzerland, 2024, pp. 18–35. doi: 10.1007/978-3-031-49272-3_2.
    36. M. Jenadeleh et al., “An Image Quality Dataset with Triplet Comparisons for Multi-dimensional Scaling.” IEEE, pp. 278–281, 2024. doi: 10.1109/qomex61742.2024.10598258.
    37. F. Huth, M. Koch, M. Awad-Mohammed, K. Kurzhals, and D. Weiskopf, “Eye Tracking on Text Reading with Visual Enhancements,” in Symposium on Eye Tracking Research and Applications, in ETRA ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 7. doi: 10.1145/3649902.3653521.
    38. T. Krake, D. Klötzl, D. Hägele, and D. Weiskopf, “Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–16, 2024, doi: 10.1109/tvcg.2024.3364388.
    39. D. Saupe, K. Rusek, D. Hägele, D. Weiskopf, and L. Janowski, “Maximum Entropy and Quantized Metric Models for Absolute Category Ratings,” IEEE Signal Processing Letters, vol. 31, pp. 2970–2974, 2024, doi: 10.1109/lsp.2024.3480832.
    40. Y. Wang, Q. Dai, M. Bâce, K. Klein, and A. Bulling, “Saliency3D: a 3D Saliency Dataset Collected on Screen,” in Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), ACM, 2024, pp. 1–6. doi: 10.1145/3649902.3653350.
    41. 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.
    42. S. P. Feyer et al., “2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualisations in Virtual Reality,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, Art. no. 1, 2024, doi: 10.1109/tvcg.2023.3327402.
  3. 2023

    1. C. Beck and M. Köllner, “GHisBERT – Training BERT from scratch for lexical semantic investigations across historical German language stages,” in Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change, N. Tahmasebi, S. Montariol, H. Dubossarsky, A. Kutuzov, S. Hengchen, D. Alfter, F. Periti, and P. Cassotti, Eds., Singapore: Association for Computational Linguistics, Dec. 2023, pp. 33–45. [Online]. Available: https://aclanthology.org/2023.lchange-1.4
    2. 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.
    3. J. Schmalfuß, L. Mehl, and A. Bruhn, “Distracting Downpour: Adversarial Weather Attacks for Motion Estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct. 2023, pp. 10106–10116. [Online]. Available: https://openaccess.thecvf.com/content/ICCV2023/html/Schmalfuss_Distracting_Downpour_Adversarial_Weather_Attacks_for_Motion_Estimation_ICCV_2023_paper.html
    4. L. Hirsch, F. Müller, F. Chiossi, T. Benga, and A. M. Butz, “My Heart Will Go On: Implicitly Increasing Social Connectedness by Visualizing Asynchronous Players’ Heartbeats in VR Games,” Proc. ACM Hum.-Comput. Interact., vol. 7, Oct. 2023, doi: 10.1145/3611057.
    5. J. Zagermann, S. Hubenschmid, D. I. Fink, J. Wieland, H. Reiterer, and T. Feuchtner, “Challenges and Opportunities for Collaborative Immersive Analytics with Hybrid User Interfaces,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Los Alamitos, CA, USA: IEEE Computer Society, Oct. 2023, pp. 191–195. doi: 10.1109/ISMAR-Adjunct60411.2023.00044.
    6. O. Wiedemann, V. Hosu, S. Su, and D. Saupe, “Konx: cross-resolution image quality assessment,” Quality and User Experience, vol. 8, Art. no. 1, Aug. 2023, doi: 10.1007/s41233-023-00061-8.
    7. 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
    8. 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
    9. G. Chen, H. Lin, O. Wiedemann, and D. Saupe, “Localization of Just Noticeable Difference for Image Compression,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), Jun. 2023, pp. 61–66. doi: 10.1109/QoMEX58391.2023.10178653.
    10. X. Zhao et al., “CUDAS: Distortion-Aware Saliency Benchmark,” IEEE Access, vol. 11, pp. 58025–58036, Jun. 2023, doi: 10.1109/access.2023.3283344.
    11. K.-T. Chen et al., “Gazealytics : A Unified and Flexible Visual Toolkit for Exploratory and Comparative Gaze Analysis,” in ETRA ’23 : Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, Association for Computing Machinery, May 2023, pp. 1–7. doi: 10.1145/3588015.3589844.
    12. M. Kern, S. Jaeger-Honz, F. Schreiber, and B. Sommer, “APL@voro—interactive visualization and analysis of cell membrane simulations,” Bioinformatics, vol. 39, Art. no. 2, Feb. 2023, doi: 10.1093/bioinformatics/btad083.
    13. 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.
    14. N. Rodrigues, C. Schulz, S. Döring, D. Baumgartner, T. Krake, and D. Weiskopf, “Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, Art. no. 1, Jan. 2023, doi: 10.1109/TVCG.2022.3209429.
    15. L. Mehl, A. Jahedi, J. Schmalfuß, and A. Bruhn, “M-FUSE: Multi-frame Fusion for Scene Flow Estimation,” in Proc. Winter Conference on Applications of Computer Vision (WACV), Jan. 2023. doi: 10.48550/arXiv.2207.05704.
    16. T. Kosch, J. Karolus, J. Zagermann, H. Reiterer, A. Schmidt, and P. W. Woźniak, “A Survey on Measuring Cognitive Workload in Human-Computer Interaction,” ACM Comput. Surv., Jan. 2023, doi: 10.1145/3582272.
    17. T. Ge et al., “Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–13, 2023, [Online]. Available: https://ieeexplore.ieee.org/document/10147241
    18. J. Wieland, “Designing and Evaluating Interactions for Handheld AR,” in Companion Proceedings of the 2023 Conference on Interactive Surfaces and Spaces, in ISS Companion ’23. New York, NY, USA: Association for Computing Machinery, 2023, pp. 100–103. doi: 10.1145/3626485.3626555.
    19. M. Gleicher, M. Riveiro, T. von Landesberger, O. Deussen, R. Chang, and C. Gillman, “A Problem Space for Designing Visualizations,” IEEE Computer Graphics and Applications, vol. 43, Art. no. 4, 2023, [Online]. Available: https://ieeexplore.ieee.org/document/10179119
    20. A. V. Reinschluessel and J. Zagermann, “Exploring Hybrid User Interfaces for Surgery Planning,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 2023, pp. 208–210. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10322244
    21. 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.
    22. F. Draxler, A. Schmidt, and L. L. Chuang, “Relevance, Effort, and Perceived Quality: Language Learners’ Experiences with AI-Generated Contextually Personalized Learning Material,” in Proceedings of the 2023 ACM Designing Interactive Systems Conference, in DIS ’23. New York, NY, USA: Association for Computing Machinery, 2023, pp. 2249–2262. doi: 10.1145/3563657.3596112.
    23. P. Paetzold, R. Kehlbeck, H. Strobelt, Y. Xue, S. Storandt, and O. Deussen, “RectEuler: Visualizing Intersecting Sets using Rectangles,” Computer Graphics Forum, vol. 42, Art. no. 3, 2023, doi: 10.1111/cgf.14814.
    24. W. Kerle-Malcharek, S. P. Feyer, F. Schreiber, and K. Klein, “GAV-VR: An Extensible Framework for Graph Analysis and Visualisation in Virtual Reality,” in ICAT-EGVE 2023 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments, J.-M. Normand, M. Sugimoto, and V. Sundstedt, Eds., The Eurographics Association, 2023. doi: 10.2312/egve.20231321.
    25. 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.
    26. C. Schneegass, M. L. Wilson, H. A. Maior, F. Chiossi, A. L. Cox, and J. Wiese, “The Future of Cognitive Personal Informatics,” in Proceedings of the 25th International Conference on Mobile Human-Computer Interaction, in MobileHCI ’23 Companion. New York, NY, USA: Association for Computing Machinery, 2023. doi: 10.1145/3565066.3609790.
    27. M. Jenadeleh, J. Zagermann, H. Reiterer, U.-D. Reips, R. Hamzaoui, and D. Saupe, “Relaxed forced choice improves performance of visual quality assessment methods,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), 2023, pp. 37–42. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10178467
    28. 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, pp. 1–17, 2023, [Online]. Available: https://ieeexplore.ieee.org/document/10158903
    29. A. Zaky, J. Zagermann, H. Reiterer, and T. Feuchtner, “Opportunities and Challenges of Hybrid User Interfaces for Optimization of Mixed Reality Interfaces,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 2023, pp. 215–219. [Online]. Available: https://ieeexplore.ieee.org/document/10322176
    30. S. Hubenschmid, J. Zagermann, D. Leicht, H. Reiterer, and T. Feuchtner, “ARound the Smartphone: Investigating the Effects of Virtually-Extended Display Size on Spatial Memory,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), New York, NY, USA: ACM, 2023. [Online]. Available: https://kops.uni-konstanz.de/server/api/core/bitstreams/6eecac2f-666f-4399-bec3-d8e607331164/content
    31. A. Jahedi, M. Luz, M. Rivinius, L. Mehl, and A. Bruhn, “MS-RAFT+: High Resolution Multi-Scale RAFT,” International Journal of Computer Vision, pp. 1573–1405, 2023, doi: 10.1007/s11263-023-01930-7.
    32. E. Pangratz, F. Chiossi, S. Villa, K. Gramann, and L. Gehrke, “Towards an Implicit Metric of Sensory-Motor Accuracy: Brain Responses to Auditory Prediction Errors in Pianists,” in Proceedings of the 15th Conference on Creativity and Cognition, in C&C ’23. New York, NY, USA: Association for Computing Machinery, 2023, pp. 129–138. doi: 10.1145/3591196.3593340.
    33. W. Teramoto and M. O. Ernst, “Effects of invisible lip movements on phonetic perception,” Scientific Reports, vol. 13, Art. no. 1, 2023, doi: 10.1038/s41598-023-33791-y.
    34. 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.
    35. 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.
    36. M. Butt, L. Carnesale, and T. Ahmed, “Experiencers vs. agents in Urdu/Hindi nominalized verbs of perception,” in Proceedings of the Lexical Functional Grammar Conference, 2023, pp. 90–113. [Online]. Available: https://lfg-proceedings.org/lfg/index.php/main/article/view/46
    37. 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, [Online]. Available: https://ieeexplore.ieee.org/document/9904619
    38. 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.
    39. 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.
    40. 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,” in 15th International Conference on Quality of Multimedia Experience (QoMEX), 2023, pp. 55–60. [Online]. Available: https://ieeexplore.ieee.org/document/10178554
    41. S. Hubenschmid, D. I. Fink, J. Zagermann, J. Wieland, H. Reiterer, and T. Feuchtner, “Colibri: A Toolkit for Rapid Prototyping of Networking Across Realities,” in 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 2023, pp. 9–13. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10322249
  4. 2022

    1. D. I. Fink, J. Zagermann, H. Reiterer, and H.-C. Jetter, “Re-Locations: Augmenting Personal and Shared Workspaces to Support Remote Collaboration in Incongruent Spaces,” Proc. ACM Hum.-Comput. Interact., vol. 6, Nov. 2022, doi: 10.1145/3567709.
    2. A. Jahedi, L. Mehl, M. Rivinius, and A. Bruhn, “Multi-Scale RAFT: combining hierarchical concepts for learning-based optical flow estimation,” in Proceedings of the IEEE International Conference on Image Processing (ICIP), Oct. 2022, pp. 1236–1240. doi: 10.48550/arXiv.2207.12163.
    3. K. Angerbauer and M. Sedlmair, “Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper),” in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), Oct. 2022, pp. 20–27. [Online]. Available: https://ieeexplore.ieee.org/document/9978448
    4. C. Müller, M. Heinemann, D. Weiskopf, and T. Ertl, “Power Overwhelming: Quantifying the Energy Cost of Visualisation,” in Proceedings of the 2022 IEEE Workshop on Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), Oct. 2022, pp. 38–46. doi: 10.1109/BELIV57783.2022.00009.
    5. J. Schmalfuß, P. Scholze, and A. Bruhn, “A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow,” Proceedings of the European Conference on Computer Vision (ECCV), Oct. 2022, doi: 10.1007/978-3-031-20047-2_11.
    6. H. Lin, H. Men, Y. Yan, J. Ren, and D. Saupe, “Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, Sep. 2022, pp. 1–4. [Online]. Available: https://ieeexplore.ieee.org/document/9900904
    7. 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.
    8. 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 2022, Chur, Switzerland, August 16-18, 2022, M. Burch, G. Wallner, and D. Limberger, Eds., ACM, Aug. 2022, pp. 1–5. doi: 10.1145/3554944.3554970.
    9. M. Zameshina et al., “Fairness in generative modeling: do it unsupervised!,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, Jul. 2022, pp. 320–323. doi: 10.1145/3520304.3528992.
    10. 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.
    11. P. Balestrucci, D. Wiebusch, and M. O. Ernst, “ReActLab: A Custom Framework for Sensorimotor Experiments “in-the-wild,”” Frontiers in Psychology, vol. 13, Jun. 2022, doi: 10.3389/fpsyg.2022.906643/full.
    12. 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.
    13. 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.
    14. M. Philipp, N. Bacher, S. Sauer, F. Mathis-Ullrich, and A. Bruhn, “From Chairs To Brains: Customizing Optical Flow For Surgical Activity Localization,” in Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI), IEEE, Mar. 2022, pp. 1–5. [Online]. Available: https://ieeexplore.ieee.org/document/9761704
    15. F. Petersen, B. Goldluecke, O. Deussen, and H. Kuehne, “Style Agnostic 3D Reconstruction via Adversarial Style Transfer,” in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, Jan. 2022, pp. 2273–2282. [Online]. Available: http://dblp.uni-trier.de/db/conf/wacv/wacv2022.html#PetersenGDK22
    16. G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair, and D. Weiskopf, “Scalability in Visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–15, 2022.
    17. D. Weiskopf, “Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization,” Frontiers in Bioinformatics, vol. 2, 2022, doi: 10.3389/fbinf.2022.793819.
    18. D. Dietz et al., “Walk This Beam: Impact of Different Balance Assistance Strategies and Height Exposure on Performance and Physiological Arousal in VR,” in 28th ACM Symposium on Virtual Reality Software and Technology, 2022, pp. 1–12. doi: 10.1145/3562939.3567818.
    19. 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, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231222004714
    20. Y. Zhang, K. Klein, O. Deussen, T. Gutschlag, and S. Storandt, “Robust Visualization of Trajectory Data,” it - Information Technology, vol. 64, pp. 181–191, 2022, doi: 10.1515/itit-2022-0036.
    21. R. Kehlbeck, J. Görtler, Y. Wang, and O. Deussen, “SPEULER: Semantics-preserving Euler Diagrams,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, Art. no. 1, 2022, [Online]. Available: https://www.computer.org/csdl/journal/tg/2022/01/09552459/1xibZ9AqsLu
    22. D. Garkov, C. Müller, M. Braun, D. Weiskopf, and F. Schreiber, “Research Data Curation in Visualization : Position Paper.” IEEE, pp. 56–65, 2022. doi: 10.1109/beliv57783.2022.00011.
    23. A. Huang, P. Knierim, F. Chiossi, L. L. Chuang, and R. Welsch, “Proxemics for Human-Agent Interaction in Augmented Reality,” in CHI Conference on Human Factors in Computing Systems, 2022, pp. 1–13. doi: 10.1145/3491102.3517593.
    24. 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.
    25. 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, [Online]. Available: https://jtcam.episciences.org/10398
    26. 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
    27. 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.
    28. 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.
    29. 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.
    30. F. Chiossi, R. Welsch, S. Villa, L. L. Chuang, and S. Mayer, “Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience,” Big Data and Cognitive Computing, vol. 6, Art. no. 2, 2022, [Online]. Available: https://www.mdpi.com/2504-2289/6/2/55
    31. F. Schreiber and D. Weiskopf, “Quantitative Visual Computing,” it - Information Technology, vol. 64, pp. 119–120, 2022, doi: 10.1515/itit-2022-0048.
    32. J. Zagermann et al., “Complementary Interfaces for Visual Computing,” it - Information Technology, vol. 64, pp. 145–154, 2022, doi: 10.1515/itit-2022-0031.
    33. 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, pp. 1–11, 2022.
    34. 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: /brokenurl#ttps://arxiv.org/abs/2210.11242
    35. 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.
    36. 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, [Online]. Available: https://ieeexplore.ieee.org/document/9873980
    37. A. Niarakis et al., “Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology,” Briefings in bioinformatics, vol. 23, Art. no. 4, 2022.
    38. T. Kosch, R. Welsch, L. L. Chuang, and A. Schmidt, “The Placebo Effect of Artificial Intelligence in Human-Computer Interaction,” ACM Transactions on Computer-Human Interaction, 2022, doi: 10.1145/3529225.
    39. 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.
    40. 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, [Online]. Available: https://ieeexplore.ieee.org/document/9745537
    41. F. Götz-Hahn, V. Hosu, and D. Saupe, “Critical Analysis on the Reproducibility of Visual Quality Assessment Using Deep Features,” PLoS ONE, vol. 17, Art. no. 8, 2022, [Online]. Available: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0269715
    42. F. Petersen, B. Goldluecke, C. Borgelt, and O. Deussen, “GenDR: A Generalized Differentiable Renderer,” in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 3992–4001. doi: 10.1109/CVPR52688.2022.00397.
    43. Y. Wang, C. Jiao, M. Bâce, and A. Bulling, “VisRecall: Quantifying Information Visualisation Recallability Via Question Answering,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, Art. no. 12, 2022, [Online]. Available: https://ieeexplore.ieee.org/document/9855227
    44. 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.
    45. C. Schneegass, V. Füseschi, V. Konevych, and F. Draxler, “Investigating the Use of Task Resumption Cues to Support Learning in Interruption-Prone Environments,” Multimodal Technologies and Interaction, vol. 6, Art. no. 1, 2022, [Online]. Available: https://www.mdpi.com/2414-4088/6/1/2
    46. 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.
    47. 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.
    48. 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.
    49. M. Becher et al., “Situated Visual Analysis and Live Monitoring for Manufacturing,” IEEE Computer Graphics and Applications, p. 1, 2022.
    50. 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, p. 1, 2022, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9765476
    51. 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.
    52. 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, 2022, [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/35254986/
    53. 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.
    54. 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.
    55. S. Hubenschmid et al., “ReLive: Bridging In-Situ and Ex-Situ Visual Analytics for Analyzing Mixed Reality User Studies,” in CHI Conference on Human Factors in Computing Systems (CHI ’22), New York, NY: ACM, 2022, pp. 1–20. doi: 10.1145/3491102.3517550.
  5. 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. B. Roziere et al., “EvolGAN: Evolutionary Generative Adversarial Networks,” in Computer Vision -- ACCV 2020, Cham: Springer International Publishing, Nov. 2021, pp. 679–694. [Online]. Available: https://openaccess.thecvf.com/content/ACCV2020/html/Roziere_EvolGAN_Evolutionary_Generative_Adversarial_Networks_ACCV_2020_paper.html
    4. R. Sevastjanova, A.-L. Kalouli, C. Beck, H. Schäfer, and M. El-Assady, “Explaining Contextualization in Language Models using Visual Analytics,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online: Association for Computational Linguistics, Aug. 2021, pp. 464–476. [Online]. Available: https://aclanthology.org/2021.acl-long.39
    5. M. Aichem et al., “Visual exploration of large metabolic models,” Bioinformatics, vol. 37, Art. no. 23, May 2021, doi: 10.1093/bioinformatics/btab335.
    6. K. Lu et al., “Palettailor: Discriminable Colorization for Categorical Data,” IEEE Transactions on Visualization & Computer Graphics, vol. 27, Art. no. 2, Feb. 2021, [Online]. Available: https://ieeexplore.ieee.org/document/9222351
    7. P. Balestrucci, V. Maffei, F. Lacquaniti, and A. Moscatelli, “The Effects of Visual Parabolic Motion on the Subjective Vertical and on Interception,” Neuroscience, vol. 453, pp. 124–137, Jan. 2021, [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0306452220306424
    8. D. Bethge et al., “VEmotion: Using Driving Context for Indirect Emotion Prediction in Real-Time,” in The 34th Annual ACM Symposium on User Interface Software and Technology, New York, NY, USA: Association for Computing Machinery, 2021, pp. 638–651. doi: 10.1145/3472749.3474775.
    9. K. Gadhave et al., “Predicting intent behind selections in scatterplot visualizations,” Information Visualization, vol. 20, Art. no. 4, 2021, doi: 10.1177/14738716211038604.
    10. K. Vock, S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “IDIAR : Augmented Reality Dashboards to Supervise Mobile Intervention Studies,” in Mensch und Computer 2021 (MuC ’21), New York, NY: ACM, 2021. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-22ydtfzvxx3l1
    11. 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,” in 32nd British Machine Vision Conference, 2021, pp. 1–12. [Online]. Available: https://www.bmvc2021-virtualconference.com/assets/papers/0868.pdf
    12. H. Ben Lahmar and M. Herschel, “Collaborative filtering over evolution provenance data for interactive visual data exploration,” Information Systems, vol. 95, p. 101620, 2021, doi: 10.1016/j.is.2020.101620.
    13. K. Klein, M. Aichem, Y. Zhang, S. Erk, B. Sommer, and F. Schreiber, “TEAMwISE : synchronised immersive environments for exploration and analysis of animal behaviour,” Journal of Visualization, 2021, doi: 10.1007/s12650-021-00746-2.
    14. B. Roziere et al., “Tarsier: Evolving Noise Injection in Super-Resolution GANs,” in 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 7028–7035. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9413318
    15. K. C. Kwan and H. Fu, “Automatic Image Checkpoint Selection for Guider-Follower Pedestrian Navigation,” Computer Graphics Forum, vol. 40, Art. no. 1, 2021, doi: 10.1111/cgf.14192.
    16. F. Draxler, C. Schneegass, J. Safranek, and H. Hussmann, “Why Did You Stop? - Investigating Origins and Effects of Interruptions during Mobile Language Learning,” in Mensch Und Computer 2021, in MuC ’21. New York, NY, USA: Association for Computing Machinery, 2021, pp. 21–33. doi: 10.1145/3473856.3473881.
    17. 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. [Online]. Available: https://kops.uni-konstanz.de/bitstream/handle/123456789/55453/Hubenschmid_2-84mm0sggczq02.pdf?sequence=1&isAllowed=y
    18. H. Men, H. Lin, M. Jenadeleh, and D. Saupe, “Subjective Image Quality Assessment with Boosted Triplet Comparisons,” IEEE Access, vol. 9, pp. 138939–138975, 2021, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9559922
    19. J. Wieland, J. Zagermann, J. Müller, and H. Reiterer, “Separation, Composition, or Hybrid? : Comparing Collaborative 3D Object Manipulation Techniques for Handheld Augmented Reality,” in 2021 IEEE International Symposium on Mixed and Augmented Reality, Piscataway, NJ: IEEE, 2021, pp. 403–412. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-ahkg9sntr33e8
    20. 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, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0097849321000406
    21. Y. Chen, K. C. Kwan, L.-Y. Wei, and H. Fu, “Autocomplete Repetitive Stroking with Image Guidance,” in SIGGRAPH Asia 2021 Technical Communications, in SA ’21 Technical Communications. New York, NY, USA: Association for Computing Machinery, 2021. doi: 10.1145/3478512.3488595.
    22. H. Lin, G. Chen, and F. W. Siebert, “Positional Encoding: Improving Class-Imbalanced Motorcycle Helmet use Classification,” in 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 1194–1198. [Online]. Available: https://ieeexplore.ieee.org/document/9506178
    23. K. Klein et al., “Visual analytics of sensor movement data for cheetah behaviour analysis,” Journal of Visualization, 2021, doi: 10.1007/s12650-021-00742-6.
    24. S. Hubenschmid, J. Zagermann, S. Butscher, and H. Reiterer, “STREAM: Exploring the Combination of Spatially-Aware Tablets with Augmented Reality Head-Mounted Displays for Immersive Analytics,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, New York, NY, USA: Association for Computing Machinery, 2021. doi: 10.1145/3411764.3445298.
    25. 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
    26. 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, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9309216
    27. 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.
    28. R. Bian et al., “Implicit Multidimensional Projection of Local Subspaces,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030368.
    29. R. Cutura, K. Angerbauer, F. Heyen, N. Hube, and M. Sedlmair, “DaRt: Generative Art using Dimensionality Reduction Algorithms,” in 2021 IEEE VIS Arts Program (VISAP), IEEE, 2021, pp. 59–72. [Online]. Available: https://ieeexplore.ieee.org/document/9622987
    30. 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
    31. F. Frieß, M. Becher, G. Reina, and T. Ertl, “Amortised Encoding for Large High-Resolution Displays,” in 2021 IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV), 2021, pp. 53–62. [Online]. Available: https://ieeexplore.ieee.org/document/9623235
    32. 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.
    33. S. Giebenhain and B. Goldlücke, “AIR-Nets: An Attention-Based Framework for Locally Conditioned Implicit Representations,” in 2021 International Conference on 3D Vision (3DV), 2021, pp. 1054–1064. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9665836
    34. F. Götz-Hahn, V. Hosu, H. Lin, and D. Saupe, “KonVid-150k : A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild,” IEEE Access, vol. 9, pp. 72139–72160, 2021, doi: 10.1109/ACCESS.2021.3077642.
    35. H. Booth and C. Beck, “Verb-second and Verb-first in the History of Icelandic,” Journal of Historical Syntax, vol. 5, Art. no. 27, 2021, [Online]. Available: https://ojs.ub.uni-konstanz.de/hs/index.php/hs/article/view/112
    36. 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, [Online]. Available: https://ieeexplore.ieee.org/document/9222035
    37. 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.
    38. 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, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2468502X21000309
    39. 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.
    40. 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
    41. 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.
    42. M. Kraus, K. Klein, J. Fuchs, D. A. Keim, F. Schreiber, and M. Sedlmair, “The Value of Immersive Visualization,” IEEE Computer Graphics and Applications (CG&A), vol. 41, Art. no. 4, 2021, doi: 10.1109/MCG.2021.3075258.
    43. 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/meta.
    44. G. J. Rijken et al., “Illegible Semantics: Exploring the Design Space of Metal Logos,” in IEEE VIS alt.VIS Workshop, 2021. [Online]. Available: https://arxiv.org/abs/2109.01688
    45. F. L. Dennig, M. T. Fischer, M. Blumenschein, J. Fuchs, D. A. Keim, and E. Dimara, “ParSetgnostics: Quality Metrics for Parallel Sets,” Computer Graphics Forum, vol. 40, Art. no. 3, 2021, doi: 10.1111/cgf.14314.
    46. M. Kraus et al., “Immersive Analytics with Abstract 3D Visualizations: A Survey,” Computer Graphics Forum, 2021, doi: 10.1111/cgf.14430.
    47. L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” in Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), Springer, 2021, pp. 140–152. [Online]. Available: https://link.springer.com/chapter/10.1007%2F978-3-030-75549-2_12
  6. 2020

    1. C. Beck, “DiaSense at SemEval-2020 Task 1: Modeling Sense Change via Pre-trained BERT Embeddings,” in Proceedings of the Fourteenth Workshop on Semantic Evaluation, Barcelona (online): International Committee for Computational Linguistics, Dec. 2020, pp. 50–58. [Online]. Available: https://www.aclweb.org/anthology/2020.semeval-1.4
    2. C. Beck, H. Booth, M. El-Assady, and M. Butt, “Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias,” in Proceedings of the 14th Linguistic Annotation Workshop, Barcelona, Spain: Association for Computational Linguistics, Dec. 2020, pp. 60–73. [Online]. Available: https://www.aclweb.org/anthology/2020.law-1.6
    3. 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
    4. V. Bruder, C. Müller, S. Frey, and T. Ertl, “On Evaluating Runtime Performance of Interactive Visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, pp. 2848–2862, Sep. 2020, [Online]. Available: https://ieeexplore.ieee.org/document/8637795
    5. M. Dias, D. Orellana, S. Vidal, L. Merino, and A. Bergel, “Evaluating a Visual Approach for Understanding JavaScript Source Code,” in Proceedings of the 28th International Conference on Program Comprehension, ACM, Jul. 2020, pp. 128–138. [Online]. Available: http://bergel.eu/MyPapers/Dias20-Hunter.pdf
    6. 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.
    7. 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.
    8. 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.
    9. O. Wiedemann, V. Hosu, H. Lin, and D. Saupe, “Foveated Video Coding for Real-Time Streaming Applications,” in 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX), 2020, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9123080
    10. M. Lan Ha, V. Hosu, and V. Blanz, “Color Composition Similarity and Its Application in Fine-grained Similarity,” in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Piscataway, NJ: IEEE, 2020, pp. 2548–2557. [Online]. Available: https://ieeexplore.ieee.org/document/9093522
    11. H. Lin, J. D. Deng, D. Albers, and F. W. Siebert, “Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning,” IEEE Access, vol. 8, pp. 162073–162084, 2020, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9184871
    12. F. Frieß, M. Braun, V. Bruder, S. Frey, G. Reina, and T. Ertl, “Foveated Encoding for Large High-Resolution Displays,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, Art. no. 2, 2020, doi: 10.1109/TVCG.2020.3030445.
    13. K. Kurzhals, M. Burch, and D. Weiskopf, “What We See and What We Get from Visualization: Eye Tracking Beyond Gaze Distributions and Scanpaths,” CoRR, 2020, [Online]. Available: https://arxiv.org/abs/2009.14515
    14. 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.
    15. V. Hosu, H. Lin, T. Szirányi, and D. Saupe, “KonIQ-10k : An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment,” IEEE Transactions on Image Processing, vol. 29, pp. 4041–4056, 2020, [Online]. Available: https://ieeexplore.ieee.org/document/8968750
    16. U. Ju, L. L. Chuang, and C. Wallraven, “Acoustic Cues Increase Situational Awareness in Accident Situations: A VR Car-Driving Study,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–11, 2020, [Online]. Available: https://ieeexplore.ieee.org/document/9261134
    17. T. Stankov and S. Storandt, “Maximum Gap Minimization in Polylines,” in Web and Wireless Geographical Information Systems - 18th International Symposium, W2GIS 2020, Wuhan, China, November 13-14, 2020, Proceedings, 2020, pp. 181–196. doi: 10.1007/978-3-030-60952-8\_19.
    18. F. Draxler, A. Labrie, A. Schmidt, and L. L. Chuang, “Augmented Reality to Enable Users in Learning Case Grammar from Their Real-World Interactions,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, ACM, 2020, pp. 410:1–410:12. doi: 10.1145/3313831.3376537.
    19. C. Schätzle and M. Butt, “Visual Analytics for Historical Linguistics: Opportunities and Challenges,” Journal of Data Mining and Digital Humanities, 2020, [Online]. Available: https://jdmdh.episciences.org/6968
    20. T. Guha et al., “ATQAM/MAST′20: Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends,” in Proceedings of the 28th ACM International Conference on Multimedia, in MM ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 4758–4760. doi: 10.1145/3394171.3421895.
    21. B. Roziere et al., “Evolutionary Super-Resolution,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, in GECCO ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 151–152. doi: 10.1145/3377929.3389959.
    22. H. Lin, M. Jenadeleh, G. Chen, U.-D. Reips, R. Hamzaoui, and D. Saupe, “Subjective Assessment of Global Picture-Wise Just Noticeable Difference,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 2020, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/document/9106058
    23. L. Merino, M. Lungu, and C. Seidl, “Unleashing the Potentials of Immersive Augmented Reality for Software Engineering,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER), 2020, pp. 517–521. [Online]. Available: https://arxiv.org/abs/2001.01223
    24. A. Kumar, P. Howlader, R. Garcia, D. Weiskopf, and K. Mueller, “Challenges in Interpretability of Neural Networks for Eye Movement Data,” in ACM Symposium on Eye Tracking Research and Applications, in ETRA ’20 Short Papers. New York, NY, USA: Association for Computing Machinery, 2020. doi: 10.1145/3379156.3391361.
    25. 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.
    26. N. Brich et al., “Visual Analysis of Multivariate Intensive Care Surveillance Data,” in Eurographics Workshop on Visual Computing for Biology and Medicine, B. Kozlíková, M. Krone, N. Smit, K. Nieselt, and R. G. Raidou, Eds., The Eurographics Association, 2020.
    27. 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.
    28. 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.
    29. 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.
    30. S. Cornelsen et al., “Drawing Shortest Paths in Geodetic Graphs,” in Graph Drawing and Network Visualization, D. Auber and P. Valtr, Eds., Cham: Springer International Publishing, 2020, pp. 333–340. doi: 10.1007/978-3-030-68766-3_26.
    31. J. Spoerhase, S. Storandt, and J. Zink, “Simplification of Polyline Bundles,” in 17th Scandinavian Symposium and Workshops on Algorithm Theory, SWAT 2020, June 22-24, 2020, Tórshavn, Faroe Islands, 2020, pp. 35:1–35:20. doi: 10.4230/LIPIcs.SWAT.2020.35.
    32. J. Zagermann, U. Pfeil, P. von Bauer, D. I. Fink, and H. Reiterer, ““It’s in my other hand!”: Studying the Interplay of Interaction Techniques and Multi-Tablet Activities,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2020, pp. 413:1–413:13. doi: 10.1145/3313831.3376540.
    33. F. Bishop, J. Zagermann, U. Pfeil, G. Sanderson, H. Reiterer, and U. Hinrichs, “Construct-A-Vis: Exploring the Free-Form Visualization Processes of Children,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, Art. no. 1, 2020, [Online]. Available: https://ieeexplore.ieee.org/document/8807271
    34. O. Wiedemann and D. Saupe, “Gaze Data for Quality Assessment of Foveated Video,” in ACM Symposium on Eye Tracking Research and Applications, in ETRA ’20 Adjunct. New York, NY, USA: Association for Computing Machinery, 2020. doi: 10.1145/3379157.3391656.
    35. 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.
    36. H. Lin et al., “SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning,” Quality and User Experience, vol. 5, Art. no. 1, 2020, doi: 10.1007/s41233-020-00034-1.
    37. 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.
    38. N. Rodrigues, C. Schulz, A. Lhuillier, and D. Weiskopf, “Cluster-Flow Parallel Coordinates: Tracing Clusters Across Subspaces,” in Proceedings of the Graphics Interface Conference (GI) (forthcoming), Canadian Human-Computer Communications Society / Société canadienne du dialogue humain-machine, 2020, pp. 0:1–0:11. doi: 10.20380/GI2020.38.
    39. 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.
    40. 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, [Online]. Available: https://link.springer.com/article/10.1007%2Fs41233-020-00037-y
    41. M. Kraus et al., “A Comparative Study of Orientation Support Tools in Virtual Reality Environments with Virtual Teleportation,” in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2020, pp. 227–238. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9284697
    42. F. Heyen et al., “ClaVis: An Interactive Visual Comparison System for Classifiers,” in Proceedings of the International Conference on Advanced Visual Interfaces (AVI), in AVI ’20. New York, NY, USA: Association for Computing Machinery, 2020, pp. 9:1–9:9. doi: 10.1145/3399715.3399814.
    43. D. R. Wahl et al., “Why We Eat What We Eat: Assessing Dispositional and In-the-Moment Eating Motives by Using Ecological Momentary Assessment,” JMIR mHealth and uHealth., vol. 8, Art. no. 1, 2020, [Online]. Available: https://mhealth.jmir.org/2020/1/e13191/
    44. H. Men, V. Hosu, H. Lin, A. Bruhn, and D. Saupe, “Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), 2020, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/document/9123096/authors#authors
    45. P. Angelini, S. Chaplick, S. Cornelsen, and G. Da Lozzo, “Planar L-Drawings of Bimodal Graphs,” in Graph Drawing and Network Visualization, D. Auber and P. Valtr, Eds., Cham: Springer International Publishing, 2020, pp. 205–219. doi: 10.1007/978-3-030-68766-3_17.
    46. H. Bast, P. Brosi, and S. Storandt, “Metro Maps on Octilinear Grid Graphs,” in Computer Graphics Forum, Hoboken, New Jersey: Wiley-Blackwell - STM, 2020, pp. 357–367. doi: 10.1111/cgf13986.
    47. M. Blumenschein, X. Zhang, D. Pomerenke, D. A. Keim, and J. Fuchs, “Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates,” Computer Graphics Forum, vol. 39, Art. no. 3, 2020, [Online]. Available: https://diglib.eg.org:443/handle/10.1111/cgf14000
    48. T. Kosch, A. Schmidt, S. Thanheiser, and L. L. Chuang, “One Does Not Simply RSVP: Mental Workload to Select Speed Reading Parameters Using Electroencephalography,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, ACM, 2020, pp. 637:1–637:13. doi: 10.1145/3313831.3376766.
    49. M. Borowski, J. Zagermann, C. N. Klokmose, H. Reiterer, and R. Rädle, “Exploring the Benefits and Barriers of Using Computational Notebooks for Collaborative Programming Assignments,” in Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE), 2020, pp. 468–474. doi: 10.1145/3328778.3366887.
    50. M. Jenadeleh, M. Pedersen, and D. Saupe, “Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition,” Sensors, vol. 20, Art. no. 5, 2020, [Online]. Available: https://www.mdpi.com/1424-8220/20/5/1308
    51. X. Zhao, H. Lin, P. Guo, D. Saupe, and H. Liu, “Deep Learning VS. Traditional Algorithms for Saliency Prediction of Distorted Images,” in 2020 IEEE International Conference on Image Processing (ICIP), 2020, pp. 156–160. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9191203
    52. D. Okanović et al., “Can a Chatbot Support Software Engineers with Load Testing? Approach and Experiences,” in Proceedings of the ACM/SPEC International Conference on Performance Engineering (ICPE), 2020, pp. 120–129. doi: 10.1145/3358960.3375792.
    53. N. Chotisarn et al., “A Systematic Literature Review of Modern Software Visualization,” Journal of Visualization, vol. 23, Art. no. 4, 2020, [Online]. Available: https://link.springer.com/article/10.1007%2Fs12650-020-00647-w
    54. P. Balestrucci et al., “Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper),” in 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV), IEEE, 2020, pp. 11–18. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9307759
    55. D. Weiskopf, “Vis4Vis: Visualization for (Empirical) Visualization Research,” in Foundations of Data Visualization, M. Chen, H. Hauser, P. Rheingans, and G. Scheuermann, Eds., Springer International Publishing, 2020, pp. 209–224. doi: 10.1007/978-3-030-34444-3_10.
    56. D. Schubring, M. Kraus, C. Stolz, N. Weiler, D. A. Keim, and H. Schupp, “Virtual Reality Potentiates Emotion and Task Effects of Alpha/Beta Brain Oscillations,” Brain Sciences, vol. 10, Art. no. 8, 2020, [Online]. Available: https://www.mdpi.com/2076-3425/10/8/537
    57. M. Sondag, W. Meulemans, C. Schulz, K. Verbeek, D. Weiskopf, and B. Speckmann, “Uncertainty Treemaps,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2020, pp. 111–120. [Online]. Available: https://ieeexplore.ieee.org/document/9086235
    58. M. Kraus et al., “Assessing 2D and 3D Heatmaps for Comparative Analysis: An Empirical Study,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2020, pp. 546:1–546:14. doi: 10.1145/3313831.3376675.
    59. F. Frieß, C. Müller, and T. Ertl, “Real-Time High-Resolution Visualisation,” in Proceedings of the Eurographics Symposium on Vision, Modeling, and Visualization (VMV), J. Krüger, M. Niessner, and J. Stückler, Eds., The Eurographics Association, 2020, pp. 127–135. doi: 10.2312/vmv.20201195.
    60. 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.
    61. R. Garcia and D. Weiskopf, “Inner-Process Visualization of Hidden States in Recurrent Neural Networks,” in Proceedings of the 13th International Symposium on Visual Information Communication and Interaction, in VINCI ’20. New York, NY, USA: Association for Computing Machinery, 2020. doi: 10.1145/3430036.3430047.
    62. L. Merino, M. Schwarzl, M. Kraus, M. Sedlmair, D. Schmalstieg, and D. Weiskopf, “Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 – 2019),” in IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2020. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9284762
    63. A. Streichert, K. Angerbauer, M. Schwarzl, and M. Sedlmair, “Comparing Input Modalities for Shape Drawing Tasks,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP), in ETRA ’20 Short Papers. ACM, 2020, pp. 1–5. doi: 10.1145/3379156.3391830.
  7. 2019

    1. T. Munz, L. L. Chuang, S. Pannasch, and D. Weiskopf, “VisME: Visual microsaccades explorer,” Journal of Eye Movement Research, vol. 12, Art. no. 6, Dec. 2019, [Online]. Available: https://bop.unibe.ch/JEMR/article/view/JEMR.12.6.5
    2. P. Balestrucci and M. O. Ernst, “Visuo-motor adaptation during interaction with a user-adaptive system,” Journal of Vision, vol. 19, p. 187a, Sep. 2019, [Online]. Available: https://jov.arvojournals.org/article.aspx?articleid=2750667
    3. C. Schulz et al., “A Framework for Pervasive Visual Deficiency Simulation,” in Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 2019, pp. 1852–1857. [Online]. Available: https://ieeexplore.ieee.org/document/9044164
    4. Y. Wang et al., “Improving the Robustness of Scagnostics,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, Art. no. 1, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8807247
    5. K. Klein et al., “Visual Analytics for Cheetah Behaviour Analysis.,” in VINCI, ACM, 2019, pp. 16:1–16:8. [Online]. Available: http://dblp.uni-trier.de/db/conf/vinci/vinci2019.html#0001JMWHBS19
    6. J. Müller, J. Zagermann, J. Wieland, U. Pfeil, and H. Reiterer, “A Qualitative Comparison Between Augmented and Virtual Reality Collaboration with Handheld Devices,” in Mensch und Computer 2019 – Tagungsband (MuC), F. Alt, A. Bulling, and T. Döring, Eds., GI, ACM, 2019, pp. 399–410. doi: 10.1145/3340764.3340773.
    7. H. Zhang, S. Frey, H. Steeb, D. Uribe, T. Ertl, and W. Wang, “Visualization of Bubble Formation in Porous Media,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, Art. no. 1, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8445644
    8. B. Sommer et al., “Tiled Stereoscopic 3D Display Wall - Concept, Applications and Evaluation,” Electronic Imaging, vol. 2019, Art. no. 3, 2019, [Online]. Available: https://www.ingentaconnect.com/content/ist/ei/2019/00002019/00000003/art00014
    9. H. Booth and C. Schätzle, “The Syntactic Encoding of Information Structure in the History of Icelandic,” in Proceedings of the LFG’19 Conference, M. Butt, T. H. King, and I. Toivonen, Eds., CSLI Publications, 2019, pp. 69–89. [Online]. Available: http://web.stanford.edu/group/cslipublications/cslipublications/LFG/LFG-2019/lfg2019-booth-schaetzle.pdf
    10. C. Schätzle, F. L. Dennig, M. Blumenschein, D. A. Keim, and M. Butt, “Visualizing Linguistic Change as Dimension Interactions,” in Proceedings of the International Workshop on Computational Approaches to Historical Language Change, 2019, pp. 272–278. [Online]. Available: https://www.aclweb.org/anthology/W19-4734.pdf
    11. K. Schatz et al., “Visual Analysis of Structure Formation in Cosmic Evolution,” in Proceedings of the IEEE Scientific Visualization Conference (SciVis), 2019, pp. 33–41. doi: 10.1109/scivis47405.2019.8968855.
    12. R. Netzel, N. Rodrigues, A. Haug, and D. Weiskopf, “Compensation of Simultaneous Orientation Contrast in Superimposed Textures,” in Proceedings of the Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), A. Kerren, C. Hurter, and J. Braz, Eds., SciTePress, 2019, pp. 48–57. [Online]. Available: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0007356800480057
    13. V. Bruder et al., “Volume-Based Large Dynamic Graph Analysis Supported by Evolution Provenance,” Multimedia Tools and Applications, vol. 78, Art. no. 23, 2019, doi: 10.1007/s11042-019-07878-6.
    14. V. Bruder, C. Schulz, R. Bauer, S. Frey, D. Weiskopf, and T. Ertl, “Voronoi-Based Foveated Volume Rendering,” in Proceedings of the Eurographics Conference on Visualization - Short Papers (EuroVis), J. Johansson, F. Sadlo, and G. E. Marai, Eds., Eurographics Association, 2019, pp. 67–71. doi: 10.2312/evs.20191172.
    15. M. Miller, X. Zhang, J. Fuchs, and M. Blumenschein, “Evaluating Ordering Strategies of Star Glyph Axes,” in Proceedings of the IEEE Visualization Conference (VIS), IEEE, 2019, pp. 91–95. [Online]. Available: https://ieeexplore.ieee.org/document/8933656
    16. S. Jaeger et al., “Challenges for Brain Data Analysis in VR Environments,” in 2019 IEEE Pacific Visualization Symposium (PacificVis), 2019, pp. 42–46. [Online]. Available: https://ieeexplore.ieee.org/document/8781584
    17. K. Klein, M. Aichem, B. Sommer, S. Erk, Y. Zhang, and F. Schreiber, “TEAMwISE: Synchronised Immersive Environments for Exploration and Analysis of Movement Data,” in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI), ACM, 2019, pp. 9:1–9:5. doi: 10.1145/3356422.3356450.
    18. C. Fan et al., “SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/document/8743204
    19. 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.
    20. J. Görtler, M. Spicker, C. Schulz, D. Weiskopf, and O. Deussen, “Stippling of 2D Scalar Fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, Art. no. 6, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8667696
    21. 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.
    22. D. Pomerenke, F. L. Dennig, D. A. Keim, J. Fuchs, and M. Blumenschein, “Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters,” in Proceedings of the IEEE Visualization Conference (VIS), IEEE, 2019, pp. 86–90. [Online]. Available: https://ieeexplore.ieee.org/document/8933706
    23. H. Lin, V. Hosu, and D. Saupe, “KADID-10k: A Large-scale Artificially Distorted IQA Database,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019, pp. 1–3. [Online]. Available: https://ieeexplore.ieee.org/document/8743252
    24. 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.
    25. L. Zhou, R. Netzel, D. Weiskopf, and C. R. Johnson, “Spectral Visualization Sharpening,” in Proceedings of the ACM Symposium on Applied Perception (SAP), S. Neyret, E. Kokkinara, M. González-Franco, L. Hoyet, D. W. Cunningham, and J. Swidrak, Eds., ACM, 2019, pp. 18:1–18:9. doi: 10.1145/3343036.3343133.
    26. 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, [Online]. Available: https://ieeexplore.ieee.org/document/8440843
    27. H. Men, H. Lin, V. Hosu, D. Maurer, A. Bruhn, and D. Saupe, “Visual Quality Assessment for Motion Compensated Frame Interpolation,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/document/8743221
    28. 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.
    29. C. Schätzle and H. Booth, “DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change,” in Proceedings of the International Workshop on Computational Approaches to Historical Language Change, Association for Computational Linguistics, 2019, pp. 126–135. [Online]. Available: https://www.aclweb.org/anthology/W19-4716
    30. K. Klein et al., “Fly with the flock : immersive solutions for animal movement visualization and analytics,” Journal of the Royal Society Interface, vol. 16, Art. no. 153, 2019, doi: 10.1098/rsif.2018.0794.
    31. V. Hosu, B. Goldlücke, and D. Saupe, “Effective Aesthetics Prediction with Multi-level Spatially Pooled Features,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9367–9375, 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8953497
    32. F. L. Dennig, T. Polk, Z. Lin, T. Schreck, H. Pfister, and M. Behrisch, “FDive: Learning Relevance Models using Pattern-based Similarity Measures,” Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), 2019, [Online]. Available: https://ieeexplore.ieee.org/document/8986940
    33. M. Aupetit, M. Sedlmair, M. M. Abbas, A. Baggag, and H. Bensmail, “Toward Perception-based Evaluation of Clustering Techniques for Visual Analytics,” in Proceedings of the IEEE Visualization Conference (VIS), IEEE, 2019, pp. 141–145. [Online]. Available: https://ieeexplore.ieee.org/document/8933620
    34. N. Silva et al., “Eye Tracking Support for Visual Analytics Systems: Foundations, Current Applications, and Research Challenges,” in Proceedings of the Symposium on Eye Tracking Research and Applications, ACM, 2019, pp. 11:1–11:9. doi: 10.1145/3314111.3319919.
  8. 2018

    1. C. Schätzle, “Dative Subjects: Historical Change Visualized,” Konstanz, 2018. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1d917i4avuz1a2
    2. T. Spinner, J. Körner, J. Görtler, and O. Deussen, “Towards an Interpretable Latent Space: An Intuitive Comparison of Autoencoders with Variational Autoencoders,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), IEEE VIS, 2018. [Online]. Available: https://thilospinner.com/towards-an-interpretable-latent-space/
    3. J. Zagermann, U. Pfeil, and H. Reiterer, “Studying Eye Movements as a Basis for Measuring Cognitive Load,” Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), pp. LBW095:1–LBW095:6, 2018, doi: 10.1145/3170427.3188628.
    4. N. Marniok and B. Goldluecke, “Real-time Variational Range Image Fusion and Visualization for Large-scale Scenes using GPU Hash Tables,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 912–920. [Online]. Available: https://ieeexplore.ieee.org/document/8354209
    5. H. Men, H. Lin, and D. Saupe, “Spatiotemporal Feature Combination Model for No-Reference Video Quality Assessment,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2018, pp. 1–3. [Online]. Available: https://ieeexplore.ieee.org/document/8463426
    6. D. Sacha et al., “SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8019867
    7. S. Oppold and M. Herschel, “Provenance for Entity Resolution,” in Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science, vol. 11017, K. Belhajjame, A. Gehani, and P. Alper, Eds., Springer International Publishing, 2018, pp. 226–230. doi: 10.1007/978-3-319-98379-0_25.
    8. L. L. Chuang and U. Pfeil, “Transparency and Openness Promotion Guidelines for HCI,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., ACM, 2018, pp. SIG04:1–SIG04:4. doi: 10.1145/3170427.3185377.
    9. S. S. Borojeni, S. C. J. Boll, W. Heuten, H. H. Bülthoff, and L. L. Chuang, “Feel the Movement: Real Motion Influences Responses to Take-Over Requests in Highly Automated Vehicles,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., ACM, 2018, pp. 246:1–246:13. doi: 10.1145/3173574.3173820.
    10. V. Yoghourdjian, T. Dwyer, K. Klein, K. Marriott, and M. Wybrow, “Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 12, 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8249874
    11. M. Scheer, H. H. Bülthoff, and L. L. Chuang, “Auditory Task Irrelevance: A Basis for Inattentional Deafness,” Human Factors, vol. 60, Art. no. 3, 2018, doi: 10.1177/0018720818760919.
    12. C. Schulz, A. Zeyfang, M. van Garderen, H. Ben Lahmar, M. Herschel, and D. Weiskopf, “Simultaneous Visual Analysis of Multiple Software Hierarchies,” in Proceedings of the IEEE Working Conference on Software Visualization (VISSOFT), IEEE, 2018, pp. 87–95. [Online]. Available: https://ieeexplore.ieee.org/document/8530134/
    13. S. Frey, “Spatio-Temporal Contours from Deep Volume Raycasting,” Computer Graphics Forum, vol. 37, Art. no. 3, 2018, doi: 10.1111/cgf.13438.
    14. 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.
    15. M. Klapperstueck et al., “Contextuwall: Multi-site Collaboration Using Display Walls,” Journal of Visual Languages & Computing, vol. 46, pp. 35–42, 2018, doi: 10.1016/j.jvlc.2017.10.002.
    16. C. Glatz, S. S. Krupenia, H. H. Bülthoff, and L. L. Chuang, “Use the Right Sound for the Right Job: Verbal Commands and Auditory Icons for a Task-Management System Favor Different Information Processes in the Brain,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., ACM, 2018, pp. 472:1–472:13. doi: 10.1145/3173574.3174046.
    17. N. Rodrigues, R. Netzel, J. Spalink, and D. Weiskopf, “Multiscale Scanpath Visualization and Filtering,” in Proceedings of the Symposium on Eye Tracking and Visualization (ETVIS), L. L. Chuang, M. Burch, and K. Kurzhals, Eds., ACM, 2018, pp. 2:1–2:5. doi: 10.1145/3205929.3205931.
    18. V. Bruder, M. Hlawatsch, S. Frey, M. Burch, D. Weiskopf, and T. Ertl, “Volume-Based Large Dynamic Graph Analytics,” in Proceedings of the International Conference Information Visualisation (IV), E. Banissi, R. Francese, M. W. McK. Bannatyne, T. G. Wyeld, M. Sarfraz, J. M. Pires, A. Ursyn, F. Bouali, N. Datia, G. Venturini, G. Polese, V. Deufemia, T. D. Mascio, M. Temperini, F. Sciarrone, D. Malandrino, R. Zaccagnino, P. Díaz, F. Papadopoulo, A. F. Anta, A. Cuzzocrea, M. Risi, U. Erra, and V. Rossano, Eds., IEEE, 2018, pp. 210–219. [Online]. Available: https://ieeexplore.ieee.org/document/8564163
    19. D. Maurer, M. Stoll, and A. Bruhn, “Directional Priors for Multi-Frame Optical Flow,” in Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, 2018, pp. 106:1–106:13. [Online]. Available: http://bmvc2018.org/contents/papers/0377.pdf
    20. M. Jenadeleh, M. Pedersen, and D. Saupe, “Realtime Quality Assessment of Iris Biometrics Under Visible Light,” in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPRW), CVPR Workshops, IEEE, 2018, pp. 443–452. [Online]. Available: https://ieeexplore.ieee.org/document/8575548
    21. M. Blumenschein et al., “SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach,” in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), R. Chang, H. Qu, and T. Schreck, Eds., IEEE, 2018, pp. 36–47. [Online]. Available: https://ieeexplore.ieee.org/document/8802486
    22. C. Müller et al., “Interactive Molecular Graphics for Augmented Reality Using HoloLens,” Journal of Integrative Bioinformatics, vol. 15, Art. no. 2, 2018.
    23. A. Nesti, G. Rognini, B. Herbelin, H. H. Bülthoff, L. L. Chuang, and O. Blanke, “Modulation of Vection Latencies in the Full-Body Illusion,” PLoS ONE, vol. 13, Art. no. 12, 2018, doi: 10.1371/journal.pone.0209189.
    24. 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.
    25. K. Hänsel, R. Poguntke, H. Haddadi, A. Alomainy, and A. Schmidt, “What to Put on the User: Sensing Technologies for Studies and Physiology Aware Systems,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., ACM, 2018, pp. 145:1–145:14. doi: 10.1145/3173574.3173719.
    26. K. Marriott et al., Immersive Analytics, vol. 11190. in Lecture Notes in Computer Science (LNCS), vol. 11190. Springer International Publishing, 2018. doi: 10.1007/978-3-030-01388-2.
    27. T. Kosch, M. Funk, A. Schmidt, and L. L. Chuang, “Identifying Cognitive Assistance with Mobile Electroencephalography: A Case Study with In-Situ Projections for Manual Assembly.,” Proceedings of the ACM on Human-Computer Interaction (ACMHCI), vol. 2, pp. 11:1–11:20, 2018, doi: 10.1145/3229093.
    28. C. Schulz, K. Schatz, M. Krone, M. Braun, T. Ertl, and D. Weiskopf, “Uncertainty Visualization for Secondary Structures of Proteins,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), IEEE, 2018, pp. 96–105. [Online]. Available: https://ieeexplore.ieee.org/document/8365980
    29. A. C. Valdez, M. Ziefle, and M. Sedlmair, “Priming and Anchoring Effects in Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 1, 2018, [Online]. Available: https://ieeexplore.ieee.org/document/8022891
    30. T. Torsney-Weir, S. Afroozeh, M. Sedlmair, and T. Möller, “Risk Fixers and Sweet Spotters: a Study of the Different Approaches to Using Visual Sensitivity Analysis in an Investment Scenario,” in Proceedings of the Eurographics Conference on Visualization (EuroVis), J. Johansson, F. Sadlo, and T. Schreck, Eds., Eurographics Association, 2018, pp. 119–123. doi: 10.5555/3290776.3290801.
    31. 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.
    32. H. Bast, P. Brosi, and S. Storandt, “Efficient Generation of Geographically Accurate Transit Maps,” in Proceedings of the ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL), F. B. Kashani, E. G. Hoel, R. H. Güting, R. Tamassia, and L. Xiong, Eds., ACM, 2018, pp. 13–22. doi: 10.1145/3274895.3274955.
    33. M. Ghaffar et al., “3D Modelling and Visualisation of Heterogeneous Cell Membranes in Blender,” in Proceedings of the 11th International Symposium on Visual Information Communication and Interaction, in VINCI ’18. New York, NY, USA: Association for Computing Machinery, 2018, pp. 64–71. doi: 10.1145/3231622.3231639.
    34. P. Knierim, V. Schwind, A. M. Feit, F. Nieuwenhuizen, and N. Henze, “Physical Keyboards in Virtual Reality: Analysis of Typing Performance and Effects of Avatar Hands,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., ACM, 2018, pp. 345:1–345:9. doi: 10.1145/3173574.3173919.
    35. A. Hautli-Janisz, C. Rohrdantz, C. Schätzle, A. Stoffel, M. Butt, and D. A. Keim, “Visual Analytics in Diachronic Linguistic Investigations,” Linguistic Visualizations, 2018.
    36. 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.
    37. D. Laupheimer, P. Tutzauer, N. Haala, and M. Spicker, “Neural Networks for the Classification of Building Use from Street-view Imagery,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 177–184, 2018, [Online]. Available: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2/177/2018/isprs-annals-IV-2-177-2018.pdf
    38. 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
    39. T. Dingler, R. Rzayev, A. S. Shirazi, and N. Henze, “Designing Consistent Gestures Across Device Types: Eliciting RSVP Controls for Phone, Watch, and Glasses,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, R. L. Mandryk, M. Hancock, M. Perry, and A. L. Cox, Eds., ACM, 2018, pp. 419:1–419:12. doi: 10.1145/3173574.3173993.
    40. M. de Ridder, K. Klein, and J. Kim, “A Review and Outlook on Visual Analytics for Uncertainties in Functional Magnetic Resonance Imaging,” Brain Informatics, vol. 5, Art. no. 2, 2018, doi: 10.1186/s40708-018-0083-0.
    41. Y. Zhu et al., “Genome-scale Metabolic Modeling of Responses to Polymyxins in Pseudomonas Aeruginosa,” GigaScience, vol. 7, Art. no. 4, 2018, doi: 10.1093/gigascience/giy021.
    42. J. Karolus, H. Schuff, T. Kosch, P. W. Woźniak, and A. Schmidt, “EMGuitar: Assisting Guitar Playing with Electromyography,” in Proceedings of the Designing Interactive Systems Conference (DIS), I. Koskinen, Y.-K. Lim, T. C. Pargman, K. K. N. Chow, and W. Odom, Eds., ACM, 2018, pp. 651–655. doi: 10.1145/3196709.3196803.
    43. J. Görtler, R. Kehlbeck, and O. Deussen, “A Visual Exploration of Gaussian Processes,” in Proceedings of the Workshop on Visualization for AI Explainability (VISxAI), 2018. [Online]. Available: https://distill.pub/2019/visual-exploration-gaussian-processes/
    44. M. Behrisch et al., “Quality Metrics for Information Visualization,” Computer Graphics Forum, vol. 37, Art. no. 3, 2018, doi: 10.1111/cgf.13446.
    45. D. Varga, D. Saupe, and T. Szirányi, “DeepRN: A Content Preserving Deep Architecture for Blind Image Quality Assessment,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2018, pp. 1–6. [Online]. Available: https://ieeexplore.ieee.org/document/8486528
    46. V. Hosu, H. Lin, and D. Saupe, “Expertise Screening in Crowdsourcing Image Quality,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2018, pp. 276–281. [Online]. Available: https://ieeexplore.ieee.org/document/8463427
    47. L. J. Debbeler, M. Gamp, M. Blumenschein, D. A. Keim, and B. Renner, “Polarized But Illusory Beliefs About Tap and Bottled Water: A Product- and Consumer-Oriented Survey and Blind Tasting Experiment,” Science of the Total Environment, vol. 643, pp. 1400–1410, 2018, doi: 10.1016/j.scitotenv.2018.06.190.
    48. H. Ben Lahmar, M. Herschel, M. Blumenschein, and D. A. Keim, “Provenance-based Visual Data Exploration with EVLIN,” in Proceedings of the Conference on Extending Database Technology (EDBT), 2018, pp. 686–689. doi: 10.5441/002/edbt.2018.85.
    49. Y. Wang et al., “A Perception-driven Approach to Supervised Dimensionality Reduction for Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, Art. no. 5, 2018, [Online]. Available: https://www.computer.org/csdl/journal/tg/2018/05/07920403/13rRUEgs2M7
    50. D. Maurer, N. Marniok, B. Goldluecke, and A. Bruhn, “Structure-from-motion-aware PatchMatch for Adaptive Optical Flow Estimation,” in Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11212, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., Springer International Publishing, 2018, pp. 575–592. doi: 10.1007/978-3-030-01237-3_35.
    51. F. Frieß, M. Landwehr, V. Bruder, S. Frey, and T. Ertl, “Adaptive Encoder Settings for Interactive Remote Visualisation on High-Resolution Displays,” in Proceedings of the IEEE Symposium on Large Data Analysis and Visualization - Short Papers (LDAV), IEEE, 2018, pp. 87–91. [Online]. Available: https://ieeexplore.ieee.org/document/8739215
    52. D. Maurer and A. Bruhn, “ProFlow: Learning to Predict Optical Flow,” in Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, 2018. [Online]. Available: http://bmvc2018.org/contents/supplementary/pdf/0277_supp.pdf
    53. 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. 2017

    1. O. Deussen, M. Spicker, and Q. Zheng, “Weighted Linde-Buzo-Gray Stippling,” ACM Transactions on Graphics, vol. 36, Art. no. 6, Nov. 2017, doi: 10.1145/3130800.3130819.
    2. 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.
    3. X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, “MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, Art. no. 1, 2017, [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8122058
    4. X. Zhang, Y. Sugano, and A. Bulling, “Everyday Eye Contact Detection Using Unsupervised Gaze Target Discovery,” in Proceedings of the ACM Symposium on User Interface Software and Technology (UIST), 2017, pp. 193–203. doi: 10.1145/3126594.3126614.
    5. H. Sattar, A. Bulling, and M. Fritz, “Predicting the Category and Attributes of Visual Search Targets Using Deep Gaze Pooling,” in Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 2740–2748. [Online]. Available: https://ieeexplore.ieee.org/document/8265534
    6. S. Egger-Lampl et al., “Crowdsourcing Quality of Experience Experiments,” D. Archambault, H. Purchase, and T. Hossfeld, Eds., in Information Systems and Applications, incl. Internet/Web, and HCI. , Springer International Publishing, 2017, pp. 154–190.
    7. 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
    8. M. Spicker, F. Hahn, T. Lindemeier, D. Saupe, and O. Deussen, “Quantifying Visual Abstraction Quality for Stipple Drawings,” in Proceedings of the Symposium on Non-Photorealistic Animation and Rendering (NPAR), ACM, Ed., Association for Computing Machinery, 2017, pp. 8:1–8:10. doi: 10.1145/3092919.3092923.
    9. 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
    10. M. Herschel, R. Diestelkämper, and H. Ben Lahmar, “A Survey on Provenance - What for? What form? What from?,” The VLDB Journal, vol. 26, pp. 881–906, 2017, doi: 10.1007/s00778-017-0486-1.
    11. 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.
    12. V. Schwind, K. Wolf, and N. Henze, “FaceMaker - A Procedural Face Generator to Foster Character Design Research,” O. Korn and N. Lee, Eds., Springer International Publishing, 2017, pp. 95–113. doi: 10.1007/978-3-319-53088-8_6.
    13. D. Fritsch, “Photogrammetrische Auswertung digitaler Bilder – Neue Methoden der Kamerakalibration, dichten Bildzuordnung und Interpretation von Punktwolken,” in Photogrammetrie und Fernerkundung, C. Heipke, Ed., in Springer Reference Naturwissenschaften (SRN). , Springer Spektrum, 2017, pp. 157–196. doi: 10.1007/978-3-662-47094-7_41.
    14. H. Booth, C. Schätzle, K. Börjars, and M. Butt, “Dative Subjects and the Rise of Positional Licensing in Icelandic,” in Proceedings of the LFG’17 Conference, 2017, pp. 104–124. [Online]. Available: http://web.stanford.edu/group/cslipublications/cslipublications/LFG/LFG-2017/lfg2017-bsbb.pdf
    15. 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.
    16. 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.
    17. C. Schulz, N. Rodrigues, K. Damarla, A. Henicke, and D. Weiskopf, “Visual Exploration of Mainframe Workloads,” in Proceedings of the SIGGRAPH Asia Symposium on Visualization, ACM, 2017, pp. 4:1–4:7. doi: 10.1145/3139295.3139312.
    18. S. Frey and T. Ertl, “Flow-Based Temporal Selection for Interactive Volume Visualization,” Computer Graphics Forum, vol. 36, Art. no. 8, 2017, doi: 10.1111/cgf.13070.
    19. S. Frey and T. Ertl, “Progressive Direct Volume-to-Volume Transformation,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, [Online]. Available: https://ieeexplore.ieee.org/document/7539644
    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. J. Iseringhausen et al., “4D Imaging through Spray-On Optics,” in ACM Transactions on Graphics, 2017, pp. 35:1–35:11. doi: 10.1145/3072959.3073589.
    22. P. Tutzauer, S. Becker, and N. Haala, “Perceptual Rules for Building Enhancements in 3d Virtual Worlds,” i-com, vol. 16, Art. no. 3, 2017, doi: 10.1515/icom-2017-0022.
    23. H. T. Nim et al., “Design Considerations for Immersive Analytics of Bird Movements Obtained by Miniaturised GPS Sensors,” in Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), Eurographics Association, 2017. doi: 10.2312/vcbm.20171234.
    24. T. Dingler, A. Schmidt, and T. Machulla, “Building Cognition-Aware Systems: A Mobile Toolkit for Extracting Time-of-Day Fluctuations of Cognitive Performance,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 1, Art. no. 3, 2017, doi: 10.1145/3132025.
    25. J. Allsop, R. Gray, H. H. Bülthoff, and L. L. Chuang, “Eye Movement Planning on Single-Sensor-Single-Indicator Displays is Vulnerable to User Anxiety and Cognitive Load,” Journal of Eye Movement Research, vol. 10, Art. no. 5, 2017, doi: 10.16910/jemr.10.5.8.
    26. Y. Abdelrahman, P. Knierim, P. W. Woźniak, N. Henze, and A. Schmidt, “See Through the Fire: Evaluating the Augmentation of Visual Perception of Firefighters Using Depth and Thermal Cameras,” in Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Symposium on Wearable Computers (UbiComp/ISWC), S. C. Lee, L. Takayama, and K. N. Truong, Eds., ACM, 2017, pp. 693–696. doi: 10.1145/3123024.3129269.
    27. J. Karolus, P. W. Woźniak, L. L. Chuang, and A. Schmidt, “Robust Gaze Features for Enabling Language Proficiency Awareness,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, G. Mark, S. R. Fussell, C. Lampe, m. c. schraefel, J. P. Hourcade, C. Appert, and D. Wigdor, Eds., ACM, 2017, pp. 2998–3010. doi: 10.1145/3025453.3025601.
    28. 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.
    29. J. Kratt, F. Eisenkeil, M. Spicker, Y. Wang, D. Weiskopf, and O. Deussen, “Structure-aware Stylization of Mountainous Terrains,” in Vision, Modeling & Visualization, M. Hullin, R. Klein, T. Schultz, and A. Yao, Eds., The Eurographics Association, 2017. doi: 10.2312/vmv20171255.
    30. M. van Garderen, B. Pampel, A. Nocaj, and U. Brandes, “Minimum-Displacement Overlap Removal for Geo-referenced Data Visualization,” Computer Graphics Forum, vol. 36, Art. no. 3, 2017.
    31. D. Maurer, A. Bruhn, and M. Stoll, “Order-adaptive and Illumination-aware Variational Optical Flow Refinement,” in Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, 2017, pp. 150:1–150:13. doi: 10.5244/C.31.150.
    32. 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.
    33. R. Diestelkämper, M. Herschel, and P. Jadhav, “Provenance in DISC Systems: Reducing Space Overhead at Runtime,” in Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP), 2017, pp. 1–13. doi: 10.5555/3183865.3183883.
    34. K. Srulijes et al., “Visualization of Eye-Head Coordination While Walking in Healthy Subjects and Patients with Neurodegenerative Diseases,” in Poster (reviewed) presented on Symposium of the International Society of Posture and Gait Research (ISPGR), 2017.
    35. 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.
    36. S. Funke, T. Mendel, A. Miller, S. Storandt, and M. Wiebe, “Map Simplification with Topology Constraints: Exactly and in Practice,” in Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX), S. P. Fekete and V. Ramachandran, Eds., SIAM, 2017, pp. 185–196. doi: 10.1137/1.9781611974768.15.
    37. M. Stein et al., “Bring it to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis,” in IEEE Transactions on Visualization and Computer Graphics, 2017, pp. 13–22. [Online]. Available: https://ieeexplore.ieee.org/document/8019849
    38. H. V. Le, V. Schwind, P. Göttlich, and N. Henze, “PredicTouch: A System to Reduce Touchscreen Latency using Neural Networks and Inertial Measurement Units,” in Proceedings of the ACM International Conference on Interactive Surfaces and Spaces (ISS), ACM, Ed., ACM, 2017, pp. 230–239. doi: 10.1145/3132272.3134138.
    39. 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.
    40. K. Kurzhals, E. Çetinkaya, Y. Hu, W. Wang, and D. Weiskopf, “Close to the Action: Eye-Tracking Evaluation of Speaker-Following Subtitles,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM, Ed., 2017, pp. 6559–6568. doi: 10.1145/3025453.3025772.
    41. P. Knierim et al., “Tactile Drones - Providing Immersive Tactile Feedback in Virtual Reality through Quadcopters,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), G. Mark, S. R. Fussell, C. Lampe, m. c. schraefel, J. P. Hourcade, C. Appert, and D. Wigdor, Eds., ACM, 2017, pp. 433–436. doi: 10.1145/3027063.3050426.
    42. P. Tutzauer and N. Haala, “Processing of Crawled Urban Imagery for Building Use Classification,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 143–149, 2017, doi: 10.5194/isprs-archives-XLII-1-W1-143-2017.
    43. C. Schätzle, M. Hund, F. L. Dennig, M. Butt, and D. A. Keim, “HistoBankVis: Detecting Language Change via Data Visualization,” in Proceedings of the NoDaLiDa 2017 Workshop Processing Historical Language, G. Bouma and Y. Adesam, Eds., Linköping University Electronic Press, 2017, pp. 32–39. [Online]. Available: https://www.aclweb.org/anthology/W17-0507
    44. L. L. Chuang, C. Glatz, and S. S. Krupenia, “Using EEG to Understand why Behavior to Auditory In-vehicle Notifications Differs Across Test Environments,” in Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), S. Boll, B. Pfleging, B. Donmez, I. Politis, and D. R. Large, Eds., ACM, 2017, pp. 123–133. doi: 10.1145/3122986.3123017.
    45. S. Frey, “Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks,” in Informatics, Multidisciplinary Digital Publishing Institute (MDPI), 2017, p. 27. doi: 10.3390/informatics4030027.
    46. M. Krone et al., “Molecular Surface Maps,” IEEE Transactions on Visualization and Computer Graphics (Proceedings of the Scientific Visualization 2016), vol. 23, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598824.
    47. J. Zagermann, U. Pfeil, D. I. Fink, P. von Bauer, and H. Reiterer, “Memory in Motion: The Influence of Gesture- and Touch-based Input Modalities on Spatial Memory,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, G. Mark, S. R. Fussell, C. Lampe, m. c. schraefel, J. P. Hourcade, C. Appert, and D. Wigdor, Eds., ACM, 2017, pp. 1899–1910. doi: 10.1145/3025453.3026001.
    48. O. Johannsen et al., “A Taxonomy and Evaluation of Dense Light Field Depth Estimation Algorithms,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Workshops, IEEE, 2017, pp. 1795–1812. [Online]. Available: https://ieeexplore.ieee.org/document/8014960
    49. D. Jäckle, M. Hund, M. Behrisch, D. A. Keim, and T. Schreck, “Pattern Trails: Visual Analysis of Pattern Transitions in Subspaces,” in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), B. Fisher, S. Liu, and T. Schreck, Eds., IEEE, 2017, pp. 1–12. [Online]. Available: https://ieeexplore.ieee.org/document/8585613
    50. M. Stoll, D. Maurer, S. Volz, and A. Bruhn, “Illumination-aware Large Displacement Optical Flow,” in Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science, M. Pelillo and E. R. Hancock, Eds., Springer International Publishing, 2017, pp. 139–154. doi: 10.1007/978-3-319-78199-0_10.
    51. M. Stoll, D. Maurer, and A. Bruhn, “Variational Large Displacement Optical Flow without Feature Matches,” in Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR). Lecture Notes in Computer Science, E. R. Hancock and M. Pelillo, Eds., Springer, 2017.
    52. G. Tkachev, S. Frey, C. Müller, V. Bruder, and T. Ertl, “Prediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisition,” in Proceedings of the Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), E. Association, Ed., Eurographics Association, 2017, pp. 11–20. doi: 10.2312/pgv.20171089.
    53. 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.
    54. 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.
    55. 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.
    56. 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.
    57. D. Fritsch and M. Klein, “3D and 4D Modeling for AR and VR App Developments,” in Proceedings of the International Conference on Virtual System & Multimedia (VSMM), 2017, pp. 1–8. [Online]. Available: https://ieeexplore.ieee.org/document/8346270
    58. 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.
    59. M. Behrisch et al., “Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, [Online]. Available: https://ieeexplore.ieee.org/document/7534849
    60. U. Gadiraju et al., “Crowdsourcing Versus the Laboratory: Towards Human-centered Experiments Using the Crowd,” D. Archambault, H. Purchase, and T. Hossfeld, Eds., in Information Systems and Applications, incl. Internet/Web, and HCI. , Springer International Publishing, 2017, pp. 6–26.
    61. N. Marniok, O. Johannsen, and B. Goldluecke, “An Efficient Octree Design for Local Variational Range Image Fusion,” in Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science, vol. 10496, V. Roth and T. Vetter, Eds., in Lecture Notes in Computer Science, vol. 10496. , Springer International Publishing, 2017, pp. 401–412. doi: 10.1007/978-3-319-66709-6_32.
    62. M. Tonsen, J. Steil, Y. Sugano, and A. Bulling, “InvisibleEye: Mobile Eye Tracking Using Multiple Low-Resolution Cameras and Learning-Based Gaze Estimation,” in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2017, pp. 106:1–106:21. doi: 10.1145/3130971.
    63. N. Rodrigues et al., “Visualization of Time Series Data with Spatial Context: Communicating the Energy Production of Power Plants,” in Proceedings of the ACM Symposium on Visual Information Communication and Interaction (VINCI), 2017, pp. 37–44. doi: 10.1145/3105971.3105982.
    64. 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
    65. V. Schwind, P. Knierim, L. L. Chuang, and N. Henze, ““Where’s Pinky?”: The Effects of a Reduced Number of Fingers in Virtual Reality,” in Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY), B. A. M. Schouten, P. Markopoulos, Z. O. Toups, P. A. Cairns, and T. Bekker, Eds., ACM, 2017, pp. 507–515. doi: 10.1145/3116595.3116596.
    66. T.-K. Machulla, L. L. Chuang, F. Kiss, M. O. Ernst, and A. Schmidt, “Sensory Amplification Through Crossmodal Stimulation,” in Proceedings of the CHI Workshop on Amplification and Augmentation of Human Perception, 2017.
    67. K. de Winkel, A. Nesti, H. Ayaz, and H. H. Bülthoff, “Neural Correlates of Decision Making on Whole Body Yaw Rotation: an fNIRS Study,” Neuroscience Letters, vol. 654, pp. 56–62, 2017, doi: 10.1016/j.neulet.2017.04.053.
    68. C. Schätzle, “Genitiv als Stilmittel in der Novelle,” Scalable Reading. Zeitschrift für Literaturwissenschaft und Linguistik (LiLi), vol. 47, pp. 125–140, 2017, doi: 10.1007/s41244-017-0043-9.
    69. M. de Ridder, K. Klein, and J. Kim, “Temporaltracks: Visual Analytics for Exploration of 4D fMRI Time-series Coactivation,” in Proceedings of the Computer Graphics International Conference (CGI), X. Mao, D. Thalmann, and M. L. Gavrilova, Eds., ACM, 2017, pp. 13:1–13:6. doi: 10.1145/3095140.3095153.
    70. M. Correll and J. Heer, “Surprise! Bayesian Weighting for De-Biasing Thematic Maps.,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017, [Online]. Available: http://dblp.uni-trier.de/db/journals/tvcg/tvcg23.html#CorrellH17
    71. A. Barth, B. Harrach, N. Hyvönen, and L. Mustonen, “Detecting Stochastic Inclusions in Electrical Impedance Tomography,” Inverse Problems, vol. 33, Art. no. 11, 2017, doi: 10.1088/1361-6420/aa8f5c.
    72. S. Funke, N. Schnelle, and S. Storandt, “URAN: A Unified Data Structure for Rendering and Navigation,” in Web and Wireless Geographical Information Systems. W2GIS 2017. Lecture Notes in Computer Science, vol. 10181, D. Brosset, C. Claramunt, X. Li, and T. Wang, Eds., 2017, pp. 66–82. doi: 10.1007/978-3-319-55998-8_5.
    73. H. Ben Lahmar and M. Herschel, “Provenance-based Recommendations for Visual Data Exploration,” in Proceedings of the USENIX Conference on Theory and Practice of Provenance (TAPP), 2017, pp. 1–7.
    74. 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
    75. 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.
    76. D. Maurer, M. Stoll, S. Volz, P. Gairing, and A. Bruhn, “A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow,” in Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science, vol. 10302, F. Lauze, Y. Dong, and A. B. Dahl, Eds., Springer International Publishing, 2017, pp. 537–549. doi: 10.1007/978-3-319-58771-4_43.
    77. 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.
    78. V. Bruder, S. Frey, and T. Ertl, “Prediction-Based Load Balancing and Resolution Tuning for Interactive Volume Raycasting,” Visual Informatics, vol. 1, Art. no. 2, 2017, doi: 10.1016/j.visinf.2017.09.001.
    79. 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. 2016

    1. S. Cheng and K. Mueller, “The Data Context Map: Fusing Data and Attributes into a Unified Display.,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, Art. no. 1, 2016, [Online]. Available: http://dblp.uni-trier.de/db/journals/tvcg/tvcg22.html#ChengM16
    2. D. Saupe, F. Hahn, V. Hosu, I. Zingman, M. Rana, and S. Li, “Crowd Workers Proven Useful: A Comparative Study of Subjective Video Quality Assessment,” in Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), 2016, pp. 1–2. [Online]. Available: https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/SaHaHo16.pdf
    3. V. Hosu, F. Hahn, O. Wiedemann, S.-H. Jung, and D. Saupe, “Saliency-driven Image Coding Improves Overall Perceived JPEG Quality,” in Proceedings of the Picture Coding Symposium (PCS), IEEE, 2016, pp. 1–5. [Online]. Available: https://www.uni-konstanz.de/mmsp/pubsys/publishedFiles/HoHaWi16.pdf
    4. 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.
    5. C. Schätzle and D. Sacha, “Visualizing Language Change: Dative Subjects in Icelandic,” in Proceedings of the LREC 2016 Workshop VisLRII: Visualization as Added Value in the Development, Use and Evaluation of Language Resources, 2016, pp. 8–15. [Online]. Available: http://www.lrec-conf.org/proceedings/lrec2016/workshops/LREC2016Workshop-VisLR%20II_Proceedings.pdf
    6. C. Schulz et al., “Generative Data Models for Validation and Evaluation of Visualization Techniques,” in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), ACM, 2016, pp. 112–124. doi: 10.1145/2993901.2993907.
    7. 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.
    8. S. Frey and T. Ertl, “Auto-Tuning Intermediate Representations for In Situ Visualization,” in Proceedings of the New York Scientific Data Summit (NYSDS), IEEE, 2016, pp. 1–10. [Online]. Available: https://ieeexplore.ieee.org/document/7747807
    9. M. Aupetit and M. Sedlmair, “SepMe: 2002 New Visual Separation Measures.,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), C. Hansen, I. Viola, and X. Yuan, Eds., IEEE, 2016, pp. 1–8. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7465244
    10. L. Lischke, S. Mayer, K. Wolf, N. Henze, H. Reiterer, and A. Schmidt, “Screen arrangements and interaction areas for large display work places,” in PerDis ’16 Proceedings of the 5th ACM International Symposium on Pervasive Displays, ACM, Ed., ACM, 2016, pp. 228–234. doi: 10.1145/2914920.2915027.
    11. 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.
    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. S. Butscher and H. Reiterer, “Applying Guidelines for the Design of Distortions on Focus+Context Interfaces,” in Proceedings of the Working Conference on Advanced Visual Interfaces (AVI), P. Buono, R. Lanzilotti, M. Matera, and M. F. Costabile, Eds., ACM, 2016, pp. 244–247. doi: 10.1145/2909132.2909284.
    14. T. Waltemate et al., “The Impact of Latency on Perceptual Judgments and Motor Performance in Closed-loop Interaction in Virtual Reality,” in Proceedings of the ACM Conference on Virtual Reality Software and Technology (VRST), D. Kranzlmüller and G. Klinker, Eds., ACM, 2016, pp. 27–35. doi: 10.1145/2993369.2993381.
    15. N. Flad, J. C. Ditz, A. Schmidt, H. H. Bülthoff, and L. L. Chuang, “Data-Driven Approaches to Unrestricted Gaze-Tracking Benefit from Saccade Filtering,” in Proceedings of the Second Workshop on Eye Tracking and Visualization (ETVIS), M. Burch, L. L. Chuang, and A. T. Duchowski, Eds., IEEE, 2016, pp. 1–5. [Online]. Available: https://ieeexplore.ieee.org/document/7851156
    16. J. Karolus, P. W. Woźniak, and L. L. Chuang, “Towards Using Gaze Properties to Detect Language Proficiency,” in Proceedings of the 9th Nordic Conference on Human-Computer Interaction (NordiCHI), New York, NY, USA: ACM, 2016, pp. 118:1–118:6. doi: 10.1145/2971485.2996753.
    17. 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
    18. K. Kurzhals, M. Hlawatsch, M. Burch, and D. Weiskopf, “Fixation-Image Charts,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), ACM, Ed., ACM, 2016, pp. 11–18. doi: 10.1145/2857491.2857507.
    19. K. Kurzhals, M. Hlawatsch, F. Heimerl, M. Burch, T. Ertl, and D. Weiskopf, “Gaze Stripes: Image-Based Visualization of Eye Tracking Data,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, Art. no. 1, 2016, doi: 10.1109/TVCG.2015.2468091.
    20. M. Herschel and M. Hlawatsch, “Provenance: On and Behind the Screens,” in Proceedings of the ACM International Conference on the Management of Data (SIGMOD), F. Özcan, G. Koutrika, and S. Madden, Eds., ACM, 2016, pp. 2213–2217. doi: 10.1145/2882903.2912568.
    21. J. Zagermann, U. Pfeil, and H. Reiterer, “Measuring Cognitive Load using Eye Tracking Technology in Visual Computing,” in Proceedings of the Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), M. Sedlmair, P. Isenberg, T. Isenberg, N. Mahyar, and H. Lam, Eds., ACM, 2016, pp. 78–85. doi: 10.1145/2993901.2993908.
    22. J. Müller, R. Rädle, and H. Reiterer, “Virtual Objects as Spatial Cues in Collaborative Mixed Reality Environments: How They Shape Communication Behavior and User Task Load,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, J. Kaye, A. Druin, C. Lampe, D. Morris, and J. P. Hourcade, Eds., ACM, 2016, pp. 1245–1249. doi: 10.1145/2858036.2858043.
    23. S. Funke, F. Krumpe, and S. Storandt, “Crushing Disks Efficiently,” in Combinatorial Algorithms. IWOCA 2016. Lecture Notes in Computer Science, vol. 9843, V. Mäkinen, S. J. Puglisi, and L. Salmela, Eds., Springer International Publishing, 2016, pp. 43–54. doi: 10.1007/978-3-319-44543-4_4.
    24. J. Hildenbrand, A. Nocaj, and U. Brandes, “Flexible Level-of-Detail Rendering for Large Graphs,” Y. Hu and M. Nöllenburg, Eds., 2016. [Online]. Available: https://link.springer.com/content/pdf/bbm%3A978-3-319-50106-2%2F1.pdf
    25. P. Tutzauer, S. Becker, T. Niese, O. Deussen, and D. Fritsch, “Understanding Human Perception of Building Categories in Virtual 3d Cities - a User Study,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), pp. 683–687, 2016, [Online]. Available: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/683/2016/isprs-archives-XLI-B2-683-2016.pdf
    26. T. Dingler, R. Rzayev, V. Schwind, and N. Henze, “RSVP on the go - Implicit Reading Support on Smart Watches Through Eye Tracking,” in Proceedings of the ACM International Symposium on Wearable Computers (ISWC), ACM, Ed., 2016, pp. 116–119. doi: 10.1145/2971763.2971794.
    27. M. Scheer, H. H. Bülthoff, and L. L. Chuang, “Steering Demands Diminish the Early-P3, Late-P3 and RON Components of the Event-Related Potential of Task-Irrelevant Environmental Sounds,” in Frontiers in Human Neuroscience, F. in Human Neuroscience, Ed., 2016, pp. 73:1–73:15. doi: 10.3389/fnhum.2016.00073.
    28. J. Zagermann, U. Pfeil, R. Rädle, H.-C. Jetter, C. N. Klokmose, and H. Reiterer, “When Tablets meet Tabletops: The Effect of Tabletop Size on Around-the-Table Collaboration with Personal Tablets,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, J. Kaye, A. Druin, C. Lampe, D. Morris, and J. P. Hourcade, Eds., ACM, 2016, pp. 5470–5481. doi: 10.1145/2858036.2858224.
    29. 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.
    30. 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.
    31. M. Correll and J. Heer, “Black Hat Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, Art. no. 1, 2016, [Online]. Available: http://idl.cs.washington.edu/files/2017-BlackHatVis-DECISIVe.pdf
    32. A. Barth and A. Stein, “Approximation and simulation of infinite-dimensional Lévy processes,” Stochastics and Partial Differential Equations: Analysis and Computations, vol. 6, Art. no. 2, 2016, doi: 10.1007/s40072-017-0109-2.
    33. X. Zhang, Y. Sugano, M. Fritz, and A. Bulling, “It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2299–2308. [Online]. Available: https://ieeexplore.ieee.org/document/8015018
    34. E. Wood, T. Baltrusaitis, L.-P. Morency, P. Robinson, and A. Bulling, “Learning an Appearance-Based Gaze Estimator from One Million Synthesised Images,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), 2016, pp. 131–138. doi: 10.1145/2857491.2857492.
    35. A. Barth, R. Bürger, I. Kröker, and C. Rohde, “Computational Uncertainty Quantification for a Clarifier-thickener Model with Several Random Perturbations: A Hybrid Stochastic Galerkin Approach,” Computers & Chemical Engineering, vol. 89, pp. 11–26, 2016, doi: 10.1016/j.compchemeng.2016.02.016.
    36. V. Hosu, F. Hahn, I. Zingman, and D. Saupe, “Reported Attention as a Promising Alternative to Gaze in IQA Tasks,” in Proceedings of the 5th ISCA/DEGA Workshop on Perceptual Quality of Systems (PQS 2016), 2016, pp. 117–121. [Online]. Available: https://www.isca-speech.org/archive/PQS_2016/abstracts/25.html
    37. D. Maurer, Y.-C. Ju, M. Breuß, and A. Bruhn, “Combining shape from shading and stereo: a variational approach for the joint estimation of depth, illumination and albedo.,” in Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, 2016.
    38. M. Greis, P. El.Agroudy, H. Schuff, T. Machulla, and A. Schmidt, “Decision-Making under Uncertainty: How the Amount of Presented Uncertainty Influences User Behavior,” in Proceedings of the 9th Nordic Conference on Human-Computer Interaction (NordiCHI), ACM, Ed., 2016. doi: 10.1145/2971485.2971535.
    39. 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.
    40. O. Johannsen, A. Sulc, N. Marniok, and B. Goldluecke, “Layered Scene Reconstruction from Multiple Light Field Camera Views,” in Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science, vol. 10113, S.-H. Lai, V. Lepetit, K. Nishino, and Y. Sato, Eds., Springer International Publishing, 2016, pp. 3–18. doi: 10.1007/978-3-319-54187-7_1.
    41. 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.
    42. S. Funke, A. Nusser, and S. Storandt, “On k-Path Covers and their Applications.,” VLDB Journal, vol. 25, Art. no. 1, 2016, doi: 10.1007/s00778-015-0392-3.
    43. 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.
    44. 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
    45. B. Pfleging, D. K. Fekety, A. Schmidt, and A. L. Kun, “A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, J. Kaye, A. Druin, C. Lampe, D. Morris, and J. P. Hourcade, Eds., ACM, 2016, pp. 5776–5788. doi: 10.1145/2858036.2858117.
    46. 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
    47. E. Wood, T. Baltrusaitis, L.-P. Morency, P. Robinson, and A. Bulling, “A 3D Morphable Eye Region Model for Gaze Estimation,” in Proceedings of the European Conference on Computer Vision (ECCV), 2016, pp. 297–313. [Online]. Available: https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_18
    48. I. Zingman, D. Saupe, O. A. B. Penatti, and K. Lambers, “Detection of Fragmented Rectangular Enclosures in Very High Resolution Remote Sensing Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, Art. no. 8, 2016, [Online]. Available: https://ieeexplore.ieee.org/document/7452408
    49. M. Hund et al., “Visual Quality Assessment of Subspace Clusterings,” in Proceedings of the KDD Workshop on Interactive Data Exploration and Analytics (IDEA), I. KDD 2016, Ed., 2016, pp. 53–62.
    50. 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
    51. 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
    52. 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.
    53. R. Netzel, M. Burch, and D. Weiskopf, “Interactive Scanpath-Oriented Annotation of Fixations,” Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications, pp. 183–187, 2016, doi: 10.1145/2857491.2857498.
    54. 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.
  11. 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. T. Chandler et al., “Immersive Analytics.” IEEE, pp. 1–8, Sep. 2015. doi: 10.1109/bdva.2015.7314296.
    3. K. Kurzhals, M. Burch, T. Pfeiffer, and D. Weiskopf, “Eye Tracking in Computer-based Visualization,” Computing in Science & Engineering, vol. 17, Art. no. 5, 2015, doi: 10.1109/MCSE.2015.93.
    4. 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.
    5. L. L. Chuang, “Error Visualization and Information-Seeking Behavior for Air-Vehicle Control,” in Foundations of Augmented Cognition. AC 2015. Lecture Notes in Computer Science, vol. 9183, D. Schmorrow and C. M. Fidopiastis, Eds., Springer, 2015, pp. 3–11. doi: 10.1007/978-3-319-20816-9_1.
    6. M. Sedlmair and M. Aupetit, “Data-driven Evaluation of Visual Quality Measures,” Computer Graphics Forum, vol. 34, Art. no. 3, 2015, doi: 10.5555/2858877.2858899.
    7. 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
    8. S. Frey, F. Sadlo, and T. Ertl, “Balanced Sampling and Compression for Remote Visualization,” in Proceedings of the SIGGRAPH Asia Symposium on High Performance Computing, ACM, 2015, pp. 1–4. doi: 10.1145/2818517.2818529.
    9. M. Hund et al., “Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion,” in Similarity Search and Applications. International Conference on Similarity Search and Applications (SISAP). Lecture Notes in Computer Science, vol. 9371, G. Amato, R. Connor, F. Falchi, and C. Gennaro, Eds., in Lecture Notes in Computer Science, vol. 9371. , Springer, Cham, 2015, pp. 307–313. [Online]. Available: https://link.springer.com/chapter/10.1007%2F978-3-319-25087-8_29
    10. L. Lischke, P. Knierim, and H. Klinke, “Mid-Air Gestures for Window Management on Large Displays,” in Mensch und Computer 2015 – Tagungsband (MuC), D. G. Oldenbourg, Ed., De Gruyter, 2015, pp. 439–442. doi: 10.1515/9783110443929-072.
    11. L. Lischke et al., “Using Space: Effect of Display Size on Users’ Search Performance,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), B. Begole, J. Kim, K. Inkpen, and W. Woo, Eds., ACM, 2015, pp. 1845–1850. doi: 10.1145/2702613.2732845.
    12. 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.
    13. N. Flad, T. Fomina, H. H. Bülthoff, and L. L. Chuang, “Unsupervised Clustering of EOG as a Viable Substitute for Optical Eye Tracking,” in Eye Tracking and Visualization: Foundations, Techniques, and Applications, M. Burch, L. L. Chuang, B. D. Fisher, A. Schmidt, and D. Weiskopf, Eds., in Mathematics and Visualization. , Springer International Publishing, 2015, pp. 151–167. doi: 10.1007/978-3-319-47024-5_9.

Project Group A

Models and Measures

 

Completed

 

Project Group B

Adaptive Algorithms

 

Completed

 

Project Group C

Interaction

 

Completed

 

Project Group D

Applications

 

Completed