Institut für Visualisierung und Interaktive Systeme

The Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart is an institute of the department of computer science in the faculty of computer science, electrical engineering and information technology. Around 70 people are conducting research and teaching in the areas of visualisation and computer graphics, human-computer interaction and cognitive systems, computer vision and pattern recognition as well as augmented and virtual reality. This DataVerse contains the research data produced by the institute in these fields. Large-scale projects – like collaborative research centres – the institute is participating in might have additional DataVerses containing data produced at VIS. Furthermore, we recommend also visiting the DataVerse of VISUS, our closely related central research institute for the area of visualisation.

Computer Vision

Collaborative Artificial Intelligence

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9,771 to 9,780 of 10,013 Results

Handwriting Inpainting Dataset Jun 22, 2022 - Computer Vision Schmalfuss, Jenny; Scheurer, Erik; Zhao, Heng; Karantzas, Nikolaos; Bruhn, Andrés; Labate, Demetrio, 2022, "Handwriting Inpainting Dataset", https://doi.org/10.18419/DARUS-2886, DaRUS, V1 The dataset contains binary handwriting masks, which are sampled from scanned pages. Based on the overlay size, the training and test datasets are divided into five size ranges: 0-5%, 5-10%, 10-15%, 15-20% and 20-25% of the image.
Mask_Dataset.zip Jun 22, 2022 - Handwriting Inpainting Dataset ZIP Archive - 373.6 MB - MD5: 2a552bd2adc7c70b8fb897371e07d558 Contains test and training dataset splits with handwiting masks. The test folder contains the subfolders test00, test05, test10, test15 and test20, with 1000, 1000, 1000, 1000 and 100 masks for the test dataset, respectively. The train folder contains the subfolders train00, train05, train10, train15 and train20, with 100.000, 100.000, 10.000, 10.0...
Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" Jun 21, 2022 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations" Wang, Yao; Bulling, Andreas, 2022, "Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"", https://doi.org/10.18419/DARUS-2826, DaRUS, V1, UNF:6:AuvgRc09o1rESd63AqlW9Q== [fileUNF] Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. We propose a question-answering paradigm to study visualisation recallability and present VisRecall -- a novel dataset consisting of 200 information visual...
10_blur.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 4.6 MB - MD5: 4f82bd8d7e56649dc799dc0153f35189
10.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 3.7 MB - MD5: 0302e172f0cdb8cef6a84f4ecfb31caf
1_blur.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 2.3 MB - MD5: 01535147f2b09ae9ce65b154af5f2ba4
1.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 1.5 MB - MD5: 617d99e6308bb96a9ae502bb01c9d0f8
2_blur.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 4.0 MB - MD5: a12312bef04db29bba518c53b386f684
2.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 3.0 MB - MD5: 46d64ab347d11ab27e2b50f2c87add58
3_blur.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 2.0 MB - MD5: bb5c7a0ff65c682c565f36f6dc3d0e7c

Handwriting Inpainting Dataset

Jun 22, 2022 - Computer Vision

Schmalfuss, Jenny; Scheurer, Erik; Zhao, Heng; Karantzas, Nikolaos; Bruhn, Andrés; Labate, Demetrio, 2022, "Handwriting Inpainting Dataset", https://doi.org/10.18419/DARUS-2886, DaRUS, V1

The dataset contains binary handwriting masks, which are sampled from scanned pages. Based on the overlay size, the training and test datasets are divided into five size ranges: 0-5%, 5-10%, 10-15%, 15-20% and 20-25% of the image.

Mask_Dataset.zip

Jun 22, 2022 - Handwriting Inpainting Dataset

ZIP Archive - 373.6 MB -

Contains test and training dataset splits with handwiting masks. The test folder contains the subfolders test00, test05, test10, test15 and test20, with 1000, 1000, 1000, 1000 and 100 masks for the test dataset, respectively. The train folder contains the subfolders train00, train05, train10, train15 and train20, with 100.000, 100.000, 10.000, 10.0...

Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

Jun 21, 2022 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"

Wang, Yao; Bulling, Andreas, 2022, "Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"", https://doi.org/10.18419/DARUS-2826, DaRUS, V1, UNF:6:AuvgRc09o1rESd63AqlW9Q== [fileUNF]

Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. We propose a question-answering paradigm to study visualisation recallability and present VisRecall -- a novel dataset consisting of 200 information visual...

10_blur.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 4.6 MB -

10.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 3.7 MB -

1_blur.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 2.3 MB -

1.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 1.5 MB -

2_blur.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 4.0 MB -

2.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 3.0 MB -

3_blur.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 2.0 MB -

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