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pro vyhledávání: '"A. Weiskopf"'
Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progressively increase the number of data points, we propose an algorithm tha
Externí odkaz:
http://arxiv.org/abs/2410.19430
The datasets of most image quality assessment studies contain ratings on a categorical scale with five levels, from bad (1) to excellent (5). For each stimulus, the number of ratings from 1 to 5 is summarized and given in the form of the mean opinion
Externí odkaz:
http://arxiv.org/abs/2410.00817
Current research provides methods to communicate uncertainty and adapts classical algorithms of the visualization pipeline to take the uncertainty into account. Various existing visualization frameworks include methods to present uncertain data but d
Externí odkaz:
http://arxiv.org/abs/2409.10217
Various standardized tests exist that assess individuals' visualization literacy. Their use can help to draw conclusions from studies. However, it is not taken into account that the test itself can create a pressure situation where participants might
Externí odkaz:
http://arxiv.org/abs/2409.08101
For the Bio+Med-Vis Challenge 2024, we propose a visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data. Our design uses three linked views: a view of the histological im
Externí odkaz:
http://arxiv.org/abs/2409.07306
Autor:
Weiskopf, Daniel
This paper revisits the role of quantitative and qualitative methods in visualization research in the context of advancements in artificial intelligence (AI). The focus is on how we can bridge between the different methods in an integrated process of
Externí odkaz:
http://arxiv.org/abs/2409.07250
Dimensionality reduction (DR) is a well-established approach for the visualization of high-dimensional data sets. While DR methods are often applied to typical DR benchmark data sets in the literature, they might suffer from high runtime complexity a
Externí odkaz:
http://arxiv.org/abs/2408.04129
We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To suppo
Externí odkaz:
http://arxiv.org/abs/2408.02679
Image thumbnails are a valuable data source for fixation filtering, scanpath classification, and visualization of eye tracking data. They are typically extracted with a constant size, approximating the foveated area. As a consequence, the focused are
Externí odkaz:
http://arxiv.org/abs/2404.18680
The interplay between text and visualization is gaining importance for media where traditional text is enriched by visual elements to improve readability and emphasize facts. In two controlled eye-tracking experiments ($N=12$), we approach answers to
Externí odkaz:
http://arxiv.org/abs/2404.05572