41 to 50 of 69 Results
Dec 16, 2021
High-dimensional data analysis requires dealing with numerous challenges, such as selecting meaningful dimensions, finding relevant projections, and removing noise. As a result, the extraction of relevant and meaningful information from high-dimensional data is a difficult proble... |
May 14, 2020
The long-term goal of this project is to quantify visual computing systems, i.e.to assess, model and ultimately predict important characteristics that have a substantial impact on user experience. |
Dec 10, 2021
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Jun 9, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Yan, Jia Jun; Rodrigues, Nils; Shao, Lin; Schreck, Tobias; Weiskopf, Daniel, 2022, "Sample Implementation for the Paper "Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection"", https://doi.org/10.18419/darus-2810, DaRUS, V1
Java source code of proof-of-concept tool used for the paper "Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection". Code by M.Sc. student Jia Jun Yan. Supervision and concepts by Nils Rodrigues, Lin Shao, T... |
Mar 22, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "Saliency3D: A 3D Saliency Dataset Collected on Screen (Dataset and Experiment Application)", https://doi.org/10.18419/darus-4101, DaRUS, V1
While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. O... |
Jan 26, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao, 2024, "SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)", https://doi.org/10.18419/darus-3884, DaRUS, V1
Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced datase... |
May 28, 2020 - SFB-TRR 161 A02 "Quantifying Visual Computing Systems"
Bruder, Valentin; Müller, Christoph; Frey, Steffen; Ertl, Thomas, 2020, "Runtime performance measurements of interactive visualisation algorithms", https://doi.org/10.18419/darus-810, DaRUS, V1
Runtime performance measurements for GPU-based direct volume rendering and GPU-based raycasting of spherical particles on ten different discrete graphics processing units from AMD and NVIDIA. The data set at hand systematically evaluates typical factors influencing performance of... |
Dec 7, 2022 - SFB-TRR 161 A01 "Uncertainty Quantification and Analysis in Visual Computing"
Görtler, Jochen; Spinner, Thilo; Weiskopf, Daniel; Deussen, Oliver, 2022, "Replication Data for: Uncertainty-Aware Principal Component Analysis", https://doi.org/10.18419/darus-2321, DaRUS, V1
This dataset contains the source code for uncertainty-aware principal component analysis (UA-PCA) and a series of images that show dimensionality reduction plots created with UA-PCA. The software is a JavaScript library for performing principal component analysis and dimensionali... |
Aug 31, 2022 - SFB-TRR 161 B04 "Adaptive Algorithms for Motion Estimation"
Mehl, Lukas; Beschle, Cedric; Barth, Andrea; Bruhn, Andrés, 2022, "Replication Data for: An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation", https://doi.org/10.18419/darus-2890, DaRUS, V1
Results of our proposed optical flow method on the Sintel and KITTI datasets. We provide the benchmark results before and after applying our refinement approach. Additionally, we provide a supplementary material to our paper with more details on the minimisation, the numerical so... |
Mar 6, 2023 - SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"
Pomerenke, David; Dennig, Frederik L.; Keim, Daniel; Fuchs, Johannes; Blumenschein, Michael, 2022, "Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"", https://doi.org/10.18419/darus-3060, DaRUS, V2, UNF:6:UBKuKSiQ9Yl4rH7r00rY3g== [fileUNF]
This is the replication data for our publication "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters." It contains the datasets and the code used to render optimized Parallel Coordinate Plots. We used the following 36 datasets for ou... |