Selected Paper Awards & Personal Awards

For more awards, please browse our news section.

All Publications

  1. 2025

    1. 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.
    2. 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.
    3. 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.
    4. Z. Wu, Y. Wang, M. Langer, and A. M. Feit, “RelEYEance: Gaze-based Assessment of Users’ AI-reliance at Run-time,” in Proceedings of the ACM on Human-Computer Interaction, ACM, Ed., May 2025, pp. 1–18. doi: 10.1145/3725841.
    5. 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.
    6. 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.
    7. 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.
  2. 2024

    1. 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.
    2. 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.
    3. 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
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
  3. 2023

    1. F. Heyen, Q. Q. Ngo, and M. Sedlmair, “Visual Overviews for Sheet Music Structure,” in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023, ISMIR, Dec. 2023, pp. 692–699. doi: 10.5281/zenodo.10265383.
    2. 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
    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. 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.
    5. 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.
    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. X. Zhao et al., “CUDAS: Distortion-Aware Saliency Benchmark,” IEEE Access, vol. 11, pp. 58025–58036, Jun. 2023, doi: 10.1109/access.2023.3283344.
    10. 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.
    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. 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.
    13. 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.
    14. 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.
    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. 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.
    17. 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.
    18. 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.
    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. 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
    21. 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
    22. 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
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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
    28. 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.
    29. 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.
    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. 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.
    32. 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.
    33. 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
    34. 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.
    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. 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
    38. 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.
    39. 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
    40. 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.
    41. 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
  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. 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.
    3. 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.
    4. 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.
    5. 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
    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. 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.
    8. 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.
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    11. 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.
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    15. 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
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    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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
    22. 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.
    23. 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
    24. F. Schreiber and D. Weiskopf, “Quantitative Visual Computing,” it - Information Technology, vol. 64, pp. 119–120, 2022, doi: 10.1515/itit-2022-0048.
    25. 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/
    26. 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
    27. 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.
    28. 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.
    29. K. Klein, M. Sedlmair, and F. Schreiber, “Immersive Analytics: An Overview,” it - Information Technology, vol. 64, pp. 155–168, 2022, doi: 10.1515/itit-2022-0037.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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
    35. 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
    36. J. Zagermann et al., “Complementary Interfaces for Visual Computing,” it - Information Technology, vol. 64, pp. 145–154, 2022, doi: 10.1515/itit-2022-0031.
    37. 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.
    38. 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.
    39. D. Weiskopf, “Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization,” Frontiers in Bioinformatics, vol. 2, 2022, doi: 10.3389/fbinf.2022.793819.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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
    45. 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
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    47. 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
    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.
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    52. 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
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  5. 2021

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    2. 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
    3. 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.
    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
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    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. 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
    9. 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
    10. 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.
    11. 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.
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    13. 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
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    15. 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.
    16. 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.
    17. 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.
    18. 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
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    20. 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
    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. 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.
    23. 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.
    24. 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
    25. 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.
    26. 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
    27. 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.
    28. 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
    29. 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.
    30. 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
    31. 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
    32. 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
    33. 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.
    34. 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
    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. 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
    37. 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
    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. 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
    40. 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
    41. 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.
    42. 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
    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. 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.
    45. 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.
    46. M. Kraus et al., “Immersive Analytics with Abstract 3D Visualizations: A Survey,” Computer Graphics Forum, 2021, doi: 10.1111/cgf.14430.
    47. 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
  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. 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
    7. 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
    8. 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
    9. 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.
    10. 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.
    11. 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.
    12. 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
    13. 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
    14. 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
    15. 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.
    16. 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.
    17. 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
    18. 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
    19. 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.
    20. 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
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
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    28. 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.
    29. 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
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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
    35. 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.
    36. 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.
    37. 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
    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. 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
    40. 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.
    41. 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.
    42. 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/
    43. 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.
    44. 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.
    45. 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
    46. 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.
    47. 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.
    48. 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
    49. 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.
    50. 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
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  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. 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
    4. 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.
    5. 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.
    6. 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
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    8. 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.
    9. 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
    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. 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.
    12. 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
    13. 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.
    14. 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.
    15. 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
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    17. 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.
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    19. 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
    20. 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
    21. 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.
    22. 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
    23. 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
    24. 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.
    25. 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
    26. 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
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  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. 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.
    3. 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
    4. 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
    5. 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
    6. 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.
    7. 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/
    8. 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.
    9. 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.
    10. 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
    11. 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.
    12. S. Frey, “Spatio-Temporal Contours from Deep Volume Raycasting,” Computer Graphics Forum, vol. 37, Art. no. 3, 2018, doi: 10.1111/cgf.13438.
    13. 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
    14. C. Müller et al., “Interactive Molecular Graphics for Augmented Reality Using HoloLens,” Journal of Integrative Bioinformatics, vol. 15, Art. no. 2, 2018.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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
    20. 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
    21. 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
    22. 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
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    24. 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.
    25. 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
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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
    33. 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.
    34. 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.
    35. 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.
    36. 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.
    37. A. Hautli-Janisz, C. Rohrdantz, C. Schätzle, A. Stoffel, M. Butt, and D. A. Keim, “Visual Analytics in Diachronic Linguistic Investigations,” Linguistic Visualizations, 2018.
    38. 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
    39. 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/
    40. 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
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    42. 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
    43. 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.
    44. 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
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    47. 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/
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  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. 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.
    3. 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.
    4. M. Burch, M. Hlawatsch, and D. Weiskopf, “Visualizing a Sequence of a Thousand Graphs (or Even More),” Computer Graphics Forum, vol. 36, Art. no. 3, 2017, doi: 10.1111/cgf.13185.
    5. D. Sacha et al., “Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, Art. no. 1, 2017.
    6. 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.
    7. 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.
    8. 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
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    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. 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.
    19. 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
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    33. 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.
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    40. 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.
    41. 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.
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    60. 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
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    65. 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.
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  10. 2016

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    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. 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.
    6. 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.
    7. 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.
    8. 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
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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
    19. 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.
    20. 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.
    21. 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
    22. 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.
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    24. 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
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    26. 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.
    27. V. Bruder, S. Frey, and T. Ertl, “Real-Time Performance Prediction and Tuning for Interactive Volume Raycasting,” in Proceedings of the SIGGRAPH Asia Symposium on Visualization, ACM, Ed., ACM, 2016, pp. 1–8. doi: 10.1145/3002151.3002156.
    28. 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
    29. 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.
    30. 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.
    31. 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
    32. 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
    33. 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
    34. 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
    35. 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.
    36. 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.
    37. 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
    38. 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
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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
    44. 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
    45. 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.
    46. 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.
    47. 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
    48. 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.
    49. 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
    50. 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.
    51. 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.
    52. 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.
    53. 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
    54. 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.
    55. 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.
  11. 2015

    1. T. Chandler et al., “Immersive Analytics.” IEEE, pp. 1–8, Sep. 2015. doi: 10.1109/bdva.2015.7314296.
    2. 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
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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
    10. 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.
    11. 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
    12. 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.
    13. M. Spicker, J. Kratt, D. Arellano, and O. Deussen, “Depth-aware Coherent Line Drawings,” in Proceedings of the SIGGRAPH Asia Symposium on Computer Graphics and Interactive Techniques, Technical Briefs, ACM, 2015, pp. 1:1–1:5. doi: 10.1145/2820903.2820909.

Project Group A

Models and Measures

 

Completed

 

Project Group B

Adaptive Algorithms

 

Completed

 

Project Group C

Interaction

 

Completed

 

Project Group D

Applications

 

Completed