2016

Renata Georgia Raidou, Freek Marcelis, Marcel Breeuwer, Meister Eduard Groeller, Anna Vilanova, and Huub M.M. van de Wetering
In Proceedings of Visual Computing in Biology and Medicine, 2016
Niels de Hoon, A.C. Jalba, Elmar Eisemann, and Anna Vilanova
In Proceedings of Visual Computing in Biology and Medicine, 2016
Olatz Castano, Ben Kybartas, and Rafael Bidarra
In Proceedings of DiGRA-FDG 2016, 2016
Noor Shaker, Antonios Liapis, Julian Togelius, Ricardo Lopes, and Rafael Bidarra
Procedural Content Generation in Games, 2016
Thomas Höllt, Fabio Miguel de Matos Ravanelli, Markus Hadwiger, and Ibrahim Hoteit
In Proceedings of EnvirVis@EuroVis, 2016
Noeska Natasja Smit, Anne C. Kraima, Daniel Jansma, Marco C. Deruiter, Elmar Eisemann, and Anna Vilanova
In Proceedings of EuroVis (Short Papers), 2016
Renata Georgia Raidou, Oscar Casares-Magaz, Ludvig Paul Muren, U.A. van der Heide, Jarle Roervik, et al.
Computer Graphics Forum, 2016
Noeska Natasja Smit, Cees-Willem Hofstede, Anne C. Kraima, Daniel Jansma, Marco C. Deruiter, et al.
In Proceedings of Eurographics (Education Papers), 2016
Fieke Taal and Rafael Bidarra
In Proceedings of UDMV, 2016
Philipp von Radziewsky, Elmar Eisemann, Hans-Peter Seidel, and Klaus Hildebrandt
Computers & Graphics, 2016
Leonardo Scandolo, Pablo Bauszat, and Elmar Eisemann
Computer Graphics Forum, 2016
Vincent van Unen, Na Li, Ilse Molendijk, Temurhan Mine, Thomas Höllt, et al.
Immunity, 2016
Three subsequent time steps of the animated pathlines in a vessel. The cutaway technique facilitates insight into the vessel during the animation of the blood flow. Furthermore, arrow glyphs represent the flow’s pathlines.
Kai Lawonn, Sylvia Glaßer, Anna Vilanova, Bernhard Preim, and Tobias Isenberg
IEEE Transactions on Visualization and Computer Graphics, 2016
Jean-Marc Thiery, Emilie Guy, Tamy Boubekeur, and Elmar Eisemann
ACM Transactions on Graphics, 2016
The proposed OPCPs (red), applied to the Venus dataset [2]: (a) Visual enhancement of small patterns between the first two dimensions of the data, i.e., small structures obstructed by a strong pattern. - (b) Facilitated identification of distinct patterns between the second and third data dimension. - (c) Improved readability of outliers, i.e., low density information areas, in the representation. - (d) Efficient and accurate selection (blue) of a specific data structure, using the proposed O-Brushing (dark blue line).
Renata Georgia Raidou, Martin Eisemann, Marcel Breeuwer, Elmar Eisemann, and Anna Vilanova
IEEE Transactions on Visualization and Computer Graphics, 2016

2015

Left full exposure, pins can be deployed anywhere on the bone/cartilage. Right limited exposure. The orthopedic surgeon paints the areas on the bone that are deemed accessible during surgery, thus limiting where pins can be deployed
Thomas Kroes, Edward R. Valstar, and Elmar Eisemann
International Journal of Computer Assisted Radiology and Surgery, 2015
Petr Kellnhofer, Tobias Ritschel, Karol Myszkowski, Elmar Eisemann, and Hans-Peter Seidel
Computer Graphics Forum, 2015
In-game view of a part of the resulting level of Figure 2
Daniel Karavolos, Anders Bouwer, and Rafael Bidarra
In Proceedings of FDG, 2015
Our initial TF for the PET dataset is defined using only a single value. After defining the α value, the TF can further adjusted if needed.
Kai Lawonn, Noeska Natasja Smit, Bernhard Preim, and Anna Vilanova
In Proceedings of Visual Computing in Biology and Medicine, 2015
A complex scene with fine details and global illumination. Left: Images rendered with PBRT [PH10] using 32 samples per pixel rendered in 2.5 minutes. Middle: Image reconstructed by our algorithm in 2.6 minutes including rendering and filtering. Right: Equal error image with 200 samples per pixel rendered in 12.7 minutes.
Pablo Bauszat, Martin Eisemann, Elmar Eisemann, and Marcus Magnor
Computer Graphics Forum, 2015