M
Martin Eisemann
Researcher at Braunschweig University of Technology
Publications - 70
Citations - 1249
Martin Eisemann is an academic researcher from Braunschweig University of Technology. The author has contributed to research in topics: Rendering (computer graphics) & Optical flow. The author has an hindex of 17, co-authored 65 publications receiving 1123 citations. Previous affiliations of Martin Eisemann include Delft University of Technology & Cologne University of Applied Sciences.
Papers
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Proceedings ArticleDOI
Combining automated analysis and visualization techniques for effective exploration of high-dimensional data
Andrada Tatu,Georgia Albuquerque,Martin Eisemann,Jörn Schneidewind,Holger Theisel,Marcus Magnork,Daniel A. Keim +6 more
TL;DR: This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations, based on features, that can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task.
Journal ArticleDOI
Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data
Andrada Tatu,Georgia Albuquerque,Martin Eisemann,Peter Bak,Holger Theisel,Marcus Magnor,Daniel A. Keim +6 more
TL;DR: This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations, and presents ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations.
Journal ArticleDOI
Guided image filtering for interactive high-quality global illumination
TL;DR: A novel path tracing pipeline based on an edge‐aware filtering method for the indirect illumination which produces visually more pleasing results without noticeable outliers and better approximates the Monte Carlo integral compared to previous methods.
Journal ArticleDOI
Adaptive Image-Space Sampling for Gaze-Contingent Real-time Rendering
TL;DR: This work proposes an algorithm that only shades visible features of the image while cost‐effectively interpolating the remaining features without affecting perceived quality, and introduces a sampling scheme that incorporates multiple aspects of the human visual system: acuity, eye motion, contrast, and brightness adaptation.
Proceedings ArticleDOI
Improving the visual analysis of high-dimensional datasets using quality measures
TL;DR: New quality measures for three popular visualization methods: Radviz, Pixel-Oriented Displays and Table Lenses are proposed and experiments show that these measures efficiently guide the visual analysis task.