activity
20152025
most citedPerception of Line Attributes for Visualization

6 citations · 9 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2025

Analyzing Generalization in Pre-Trained Symbolic Regression

Henrik Voigt, Paul Kahlmeyer, Kai Lawonn +2

Symbolic regression algorithms search a space of mathematical expressions for formulas that explain given data. Transformer-based models have emerged as a promising, scalable appro…

physics.flu-dyn2024

Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity

Pepe Eulzer, Kevin Richter, Anna Hundertmark +3

The emergence of computational fluid dynamics (CFD) enabled the simulation of intricate transport processes, including flow in physiological structures, such as blood vessels. Whil…

cs.HC20236 cited

Perception of Line Attributes for Visualization

Anna Sterzik, Nils Lichtenberg, Jana Wilms +3

Line attributes such as width and dashing are commonly used to encode information. However, many questions on the perception of line attributes remain, such as how many levels of a…

cs.GR2023

Enhancing Vascular Analysis with Distance Visualizations: An Overview and Implementation

Jan Hombeck, Monique Meuschke, Simon Lieb +8

In recent years, the use of expressive surface visualizations in the representation of vascular structures has gained significant attention. These visualizations provide a comprehe…

cs.CV20231 cited

Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions

Henrik Voigt, Jan Hombeck, Monique Meuschke +2

Existing language and vision models achieve impressive performance in image-text understanding. Yet, it is an open question to what extent they can be used for language understandi…

cs.GR20152 cited

Feature Lines for Illustrating Medical Surface Models: Mathematical Background and Survey

Kai Lawonn, Bernhard Preim

This paper provides a tutorial and survey for a specific kind of illustrative visualization technique: feature lines. We examine different feature line methods. For this, we provid…