Here we provide a complete list of SFB-TRR 161 publications.

All Publications

  1. 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.
  2. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “ProSeCo: Visual analysis of class separation measures and dataset characteristics,” Computers & Graphics, vol. 96, pp. 48–60, 2021, doi: https://doi.org/10.1016/j.cag.2021.03.004.
  3. R. Bian et al., “Implicit Multidimensional Projection of Local Subspaces,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030368.
  4. H. Booth and C. Beck, “Verb-second and Verb-first in the History of Icelandic,” Journal of Historical Syntax, vol. 5, no. 27, Art. no. 27, 2021, doi: 10.18148/hs/2021.v5i28.112.
  5. C. Bu et al., “SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030404.
  6. M. Burch, W. Huang, M. Wakefield, H. C. Purchase, D. Weiskopf, and J. Hua, “The State of the Art in Empirical User Evaluation of Graph Visualizations,” IEEE Access, vol. 9, pp. 4173–4198, 2021, doi: 10.1109/ACCESS.2020.3047616.
  7. 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.
  8. 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.
  9. 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.
  10. K. Klein et al., “Visual analytics of sensor movement data for cheetah behaviour analysis,” Journal of Visualization, 2021, doi: 10.1007/s12650-021-00742-6.
  11. K. C. Kwan and H. Fu, “Automatic Image Checkpoint Selection for Guider-Follower Pedestrian Navigation,” Computer Graphics Forum, vol. 40, no. 1, Art. no. 1, 2021, doi: https://doi.org/10.1111/cgf.14192.
  12. K. Lu et al., “Palettailor: Discriminable Colorization for Categorical Data,” IEEE Transactions on Visualization & Computer Graphics, vol. 27, no. 02, Art. no. 02, 2021, doi: 10.1109/TVCG.2020.3030406.
  13. L. Mehl, C. Beschle, A. Barth, and A. Bruhn, “An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation,” in Proc. International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2021, pp. 140--152. doi: 10.1007/978-3-030-75549-2_12.
  14. B. Roziere et al., “Tarsier: Evolving Noise Injection in Super-Resolution GANs,” in 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 7028–7035. doi: 10.1109/ICPR48806.2021.9413318.
  15. L. Zhou, C. R. Johnson, and D. Weiskopf, “Data-Driven Space-Filling Curves,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030473.
  16. 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), 2020, pp. 1–5. doi: 10.2312/eurova.20201079.
  17. F. Bishop, J. Zagermann, U. Pfeil, G. Sanderson, H. Reiterer, and U. Hinrichs, “Construct-A-Vis: Exploring the Free-Form Visualization Processes of Children,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, Art. no. 1, 2020, doi: 10.1109/TVCG.2019.2934804.
  18. 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, no. 3, Art. no. 3, 2020, doi: 10.1111/cgf.14002.
  19. 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, no. 3, Art. no. 3, 2020, doi: 10.1111/cgf.14000.
  20. M. Blumenschein, “Pattern-Driven Design of Visualizations for High-Dimensional Data,” Universität Konstanz, Konstanz, 2020. [Online]. Available: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-18wp9dhmhapww8
  21. 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.
  22. N. Brich et al., “Visual Analysis of Multivariate Intensive Care Surveillance Data,” in Eurographics Workshop on Visual Computing for Biology and Medicine, 2020. doi: 10.2312/vcbm.20201174.
  23. 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, 2020, doi: 10.1109/TVCG.2019.2898435.
  24. N. Chotisarn et al., “A Systematic Literature Review of Modern Software Visualization,” Journal of Visualization, vol. 23, no. 4, Art. no. 4, 2020, doi: 10.1007/s12650-020-00647-w.
  25. 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, Jul. 2020, pp. 128–138. doi: https://doi.org/10.1145/3387904.3389275.
  26. F. Draxler, A. Labrie, A. Schmidt, and L. L. Chuang, “Augmented Reality to Enable Users in Learning Case Grammar from Their Real-World Interactions,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2020, pp. 410:1-410:12. doi: 10.1145/3313831.3376537.
  27. 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, no. 2, Art. no. 2, 2020, doi: 10.1109/TVCG.2020.3030445.
  28. F. Frieß, C. Müller, and T. Ertl, “Real-Time High-Resolution Visualisation,” in Proceedings of the Eurographics Symposium on Vision, Modeling, and Visualization (VMV), 2020, pp. 127–135. doi: 10.2312/vmv.20201195.
  29. 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, Seattle, WA, USA, 2020, pp. 4758–4760. doi: 10.1145/3394171.3421895.
  30. F. Heyen et al., “ClaVis: An Interactive Visual Comparison System for Classifiers,” in Proceedings of the International Conference on Advanced Visual Interfaces, 2020, pp. 9:1-9:9. doi: 10.1145/3399715.3399814.
  31. V. Hosu et al., “From Technical to Aesthetics Quality Assessment and Beyond: Challenges and Potential,” in Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, Seattle, WA, USA, 2020, pp. 19–20. doi: 10.1145/3423268.3423589.
  32. M. Jenadeleh, M. Pedersen, and D. Saupe, “Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition,” Sensors, vol. 20, no. 5, Art. no. 5, 2020, doi: 10.3390/s20051308.
  33. 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, 2020, pp. 637:1-637:13. doi: 10.1145/3313831.3376766.
  34. M. Kraus et al., “A Comparative Study of Orientation Support Tools in Virtual Reality Environments with Virtual Teleportation,” in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2020, pp. 227–238. doi: 10.1109/ISMAR50242.2020.00046.
  35. 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.
  36. 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,” Stuttgart, Germany, 2020. doi: 10.1145/3379157.3391988.
  37. A. Kumar, P. Howlader, R. Garcia, D. Weiskopf, and K. Mueller, “Challenges in Interpretability of Neural Networks for Eye Movement Data,” Stuttgart, Germany, 2020. doi: 10.1145/3379156.3391361.
  38. K. Kurzhals, M. Burch, and D. Weiskopf, “What We See and What We Get from Visualization: Eye Tracking Beyond Gaze Distributions and Scanpaths,” CoRR, vol. abs/2009.14515, 2020, [Online]. Available: https://arxiv.org/abs/2009.14515
  39. 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.
  40. K. Kurzhals et al., “Visual Analytics and Annotation of Pervasive Eye Tracking Video,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), Stuttgart, Germany, 2020, pp. 16:1-16:9. doi: 10.1145/3379155.3391326.
  41. M. Lan Ha, V. Hosu, and V. Blanz, “Color Composition Similarity and Its Application in Fine-grained Similarity,” in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Piscataway, NJ, 2020, pp. 2548--2557. doi: 10.1109/WACV45572.2020.9093522.
  42. H. Lin, M. Jenadeleh, G. Chen, U. 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. doi: 10.1109/ICMEW46912.2020.9106058.
  43. 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, doi: 10.1109/ACCESS.2020.3021357.
  44. H. Lin et al., “SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning,” CoRR, vol. abs/2001.02002, 2020, doi: 10.1007/s41233-020-00034-1.
  45. 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. doi: 10.1109/QoMEX48832.2020.9123096.
  46. 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, no. 1, Art. no. 1, 2020, doi: 10.1007/s41233-020-00037-y.
  47. 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. doi: 10.1109/SANER48275.2020.9054812.
  48. 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, p. LBW087:1–LBW087:7. doi: 10.1145/3334480.3383017.
  49. L. Merino, M. Schwarzl, M. Kraus, M. Sedlmair, D. Schmalstieg, and D. Weiskopf, “Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 -- 2019),” 2020. doi: doi: 10.1109/ISMAR50242.2020.00069.
  50. D. Okanovic 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.
  51. 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), Stuttgart, Germany, 2020, pp. 50:1-50:5. doi: 10.1145/3379156.3391829.
  52. N. Patkar, L. Merino, and O. Nierstrasz, “Towards Requirements Engineering with Immersive Augmented Reality,” in Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming, Porto, Portugal, 2020, pp. 55–60. doi: 10.1145/3397537.3398472.
  53. 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), 2020, pp. 0:1-0:11. [Online]. Available: https://openreview.net/forum?id=oVHjlwLkl-
  54. B. Roziere et al., “Evolutionary Super-Resolution,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Cancún, Mexico, 2020, pp. 151–152. doi: 10.1145/3377929.3389959.
  55. B. Roziere et al., “EvolGAN: Evolutionary Generative Adversarial Networks,” 2020. doi: 10.1007/978-3-030-69538-5_41.
  56. D. Schubring, M. Kraus, C. Stolz, N. Weiler, D. A. Keim, and H. Schupp, “Virtual Reality Potentiates Emotion and Task Effects of Alpha/Beta Brain Oscillations,” Brain Sciences, vol. 10, no. 8, Art. no. 8, 2020, doi: 10.3390/brainsci10080537.
  57. C. Schätzle and M. Butt, “Visual Analytics for Historical Linguistics: Opportunities and Challenges,” Journal of Data Mining and Digital Humanities, 2020, doi: 10.46298/jdmdh.6707.
  58. M. Sondag, W. Meulemans, C. Schulz, K. Verbeek, D. Weiskopf, and B. Speckmann, “Uncertainty Treemaps,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), 2020, pp. 111–120. doi: 10.1109/PacificVis48177.2020.7614.
  59. 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), 2020, no. 51, pp. 1–5. doi: 10.1145/3379156.3391830.
  60. 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, no. 1, Art. no. 1, 2020, doi: doi:10.2196/13191.
  61. 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.
  62. 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. doi: 10.1109/QoMEX48832.2020.9123080.
  63. O. Wiedemann and D. Saupe, “Gaze Data for Quality Assessment of Foveated Video,” Stuttgart, Germany, 2020. doi: 10.1145/3379157.3391656.
  64. J. Zagermann, U. Pfeil, P. von Bauer, D. 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.
  65. 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. doi: 10.1109/ICIP40778.2020.9191203.
  66. 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, no. 6, Art. no. 6, 2020, doi: 10.1109/TVCG.2020.2970522.
  67. S. Öney et al., “Evaluation of Gaze Depth Estimation from Eye Tracking in Augmented Reality,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Paper (ETRA-SP), 2020, pp. 49:1-49:5. doi: 10.1145/3379156.3391835.
  68. 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), 2019, pp. 141–145. doi: 10.1109/VISUAL.2019.8933620.
  69. 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), 2019, pp. 379–387. doi: 10.1145/3342197.3344515.
  70. H. Booth and C. Schätzle, “The Syntactic Encoding of Information Structure in the History of Icelandic,” in Proceedings of the LFG’19 Conference, 2019, pp. 69–89. [Online]. Available: http://web.stanford.edu/group/cslipublications/cslipublications/LFG/LFG-2019/lfg2019-booth-schaetzle.pdf
  71. 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), 2019, pp. 67–71. doi: 10.2312/evs.20191172.
  72. 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), 2019, pp. 12:1-12:9. doi: 10.1145/3314111.3319812.
  73. V. Bruder et al., “Volume-Based Large Dynamic Graph Analysis Supported by Evolution Provenance,” Multimedia Tools and Applications, vol. 78, no. 23, Art. no. 23, 2019, doi: 10.1007/s11042-019-07878-6.
  74. 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.
  75. 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, doi: 10.1109/VAST47406.2019.8986940.
  76. 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), 2019, pp. 1–6. doi: 10.1109/QoMEX.2019.8743204.
  77. 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, no. 6, Art. no. 6, 2019, doi: 10.1109/TVCG.2019.2903945.
  78. V. Hosu, H. Lin, T. Sziranyi, and D. Saupe, “KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment,” CoRR, vol. abs/1910.06180, 2019, doi: 10.1109/TIP.2020.2967829.
  79. 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, doi: 10.1109/CVPR.2019.00960.
  80. 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), 2019, pp. 9:1-9:5. doi: 10.1145/3356422.3356450.
  81. 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), 2019, pp. 1–3. doi: 10.1109/QoMEX.2019.8743252.
  82. 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), 2019, pp. 1–6. doi: 10.1109/QoMEX.2019.8743221.
  83. M. Miller, X. Zhang, J. Fuchs, and M. Blumenschein, “Evaluating Ordering Strategies of Star Glyph Axes,” in Proceedings of the IEEE Visualization Conference (VIS), 2019, pp. 91–95. doi: 10.1109/VISUAL.2019.8933656.
  84. 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), 2019, pp. 399–410. doi: 10.1145/3340764.3340773.
  85. 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), 2019, vol. 3: IVAPP, pp. 48–57. doi: 10.5220/0007356800480057.
  86. D. Pomerenke, F. L. Dennig, D. A. Keim, J. Fuchs, and M. Blumenschein, “Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters,” in Proceedings of the IEEE Visualization Conference (VIS), 2019, pp. 86–90. doi: 10.1109/VISUAL.2019.8933706.
  87. K. Schatz et al., “Visual Analysis of Structure Formation in Cosmic Evolution,” in Proceedings of the IEEE Scientific Visualization Conference (SciVis), 2019, pp. 33–41. doi: 10.1109/scivis47405.2019.8968855.
  88. 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, 2019, pp. 126–135. doi: 10.18653/v1/W19-4716.
  89. C. Schätzle, F. L. Denning, 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. doi: 10.18653/v1/W19-4734.
  90. N. Silva et al., “Eye Tracking Support for Visual Analytics Systems: Foundations, Current Applications, and Research Challenges,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), 2019, pp. 11:1-11:9. doi: 10.1145/3314111.3319919.
  91. Y. Wang, Z. Wang, C.-W. Fu, H. Schmauder, O. Deussen, and D. Weiskopf, “Image-Based Aspect Ratio Selection.,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2018.2865266.
  92. Y. Wang et al., “Improving the Robustness of Scagnostics,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2019.2934796.
  93. 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, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2018.2864506.
  94. L. Zhou, R. Netzel, D. Weiskopf, and C. R. Johnson, “Spectral Visualization Sharpening.,” in Proceedings of the ACM Symposium on Applied Perception (SAP), 2019, pp. 18:1-18:9. doi: 10.1145/3343036.3343133.
  95. 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), 2018, pp. 13–22. doi: 10.1145/3274895.3274955.
  96. M. Behrisch et al., “Quality Metrics for Information Visualization,” Computer Graphics Forum, vol. 37, no. 3, Art. no. 3, 2018, doi: https://doi.org/10.1111/cgf.13446.
  97. 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.
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