2012

Afterimage simulation of a traffic light over time. Note the over-time change of colors, blur and shape in the afterimage
Tobias Ritschel and Elmar Eisemann
Computer Graphics Forum, 2012
Francesco Banterle, Alessandro Artusi, Tunc Aydin, Piotr Didyk, Elmar Eisemann, et al.
In Proceedings of Eurographics (Tutorials), 2012
Orthopaedic workflow for hip prosthesis replacement: A 3D CT-scan (a) is segmented into labels (b). This discrete volume image is converted to a volume mesh (c). Based on medical simulations (d) [1], optimal implant design and positioning can be determined.
Christian Kehl, Daniel F. Malan, and Elmar Eisemann
In Proceedings of 3D NordOst Workshop, 2012
When stereo content (a; b) is manipulated (c), we quantify the perceived change considering luminance, and disparity (d), whereas previous work leads to wrong predictions (e) e. g., for low-texture areas, fog, or depth-of-field (arrows).Please note that all images in the paper, except for disparity and response maps are presented in anaglyph colors.
Piotr Didyk, Tobias Ritschel, Elmar Eisemann, Karol Myszkowski, Hans-Peter Seidel, and Wojciech Matusik
ACM Transactions on Graphics, 2012
Spatial querying through sphere selection. A distance query reveals the literature and anatomical landmarks associated to the structures within the selection sphere.
Noeska Natasja Smit, Anne C. Kraima, Daniel Jansma, Marco C. Deruiter, and Charl P. Botha
In Proceedings of EuroVis (Short Papers), 2012
A distance query returns all information related to the structures inside the sphere.
Noeska Natasja Smit, Anne C. Kraima, Daniel Jansma, Marco C. Deruiter, and Charl P. Botha
In Proceedings of 3D Physiological Human Workshop, 2012
From left to right: Starting from an original depth map a pixel disparity map is computed and then a disparity pyramid is built. After multi-resolution disparity processing, the dynamic range of disparity is adjusted and the resulting enhanced disparity map is produced. The map is then used to create enhanced stereo image.
Piotr Didyk, Tobias Ritschel, Elmar Eisemann, Karol Myszkowski, and Hans-Peter Seidel
In Proceedings of Human Vision and Electronic Imaging, 2012
A temporal maximum speed volume (tMSV) takes the maximum blood-flow speed for each voxel throughout the cardiac cycle. An initial surface can be extracted from this static representation of the blood-flow regions. A standard marching cubes algorithm is employed to generate the iso-surface
R.F.P. van Pelt, Thuc Nghi Nguyen, B.M. ter Haar Romeny, and Anna Vilanova
International Journal of Computer Assisted Radiology and Surgery, 2012
Overview of the extraction approach: Based on the flow grid and the aneurysm ostium we seed streamlines at the ostium into the aneurysm. We identify several local line properties on the streamlines to compute a quality scalar field on the ostium. Given this scalar field we extract a seeding curve on the ostium, which is used to construct the boundary contour of the inflow jet. A second scalar field is computed on the aneurysm surface to indicate the impingement zone. Finally, we visualize both information expressively.
R. Gasteiger, D. Lehmann, R.F.P. van Pelt, G. Janiga, O. Beuing, et al.
IEEE Transactions on Visualization and Computer Graphics, 2012

2011

Boudewijn P. F. Lelieveldt, Charl P. Botha, E.L. Kaijzel, E.A. Hendriks, J.H.C. Reiber, et al.
In Proceedings of ICASSP, 2011
Tim Tutenel, Roland van der Linden, Martin Kraus, Bart Bollen, and Rafael Bidarra
In Proceedings of PCG Workshop, 2011
Possible game scenario with lighting using bent normals: 2048×1024 pixels, 60.0 fps, including direct light and DOF on an Nvidia GF 560Ti. Environment mapping produces natural illumination, while bent normals cause colored shadows.
Oliver Klehm, Tobias Ritschel, Elmar Eisemann, and Hans-Peter Seidel
In Proceedings of Vision, Modeling, and Visualization, 2011
This figure shows glyph visualizations of HARDI and DTI-images of a 2D-slice in the brain where neural fibers in the corona radiata cross with neural fibers in the corpus callosum. Here DTI and HARDI are visualized differently; HARDI is visualized according to Def. 1, whereas DTI is visualized using Eq. (1).
R. Duits, T.C.J. Dela Haije, A. Ghosh, E.J. Creusen, Anna Vilanova, and B.M. ter Haar Romeny
In Proceedings of SSVM, 2011
Ruben M. Smelik, Tim Tutenel, Klaas Jan de Kraker, and Rafael Bidarra
Computers & Graphics, 2011
Piotr Didyk, Tobias Ritschel, Elmar Eisemann, Karol Myszkowski, and Hans-Peter Seidel
ACM Transactions on Graphics, 2011
Ricardo Lopes and Rafael Bidarra
In Proceedings of Advances in Computer Entertainment Technology, 2011
Daniel Scherzer, Lei Yang, Oliver Mattausch, Diego Nehab, Pedro V. Sander, et al.
In Proceedings of Eurographics (State of the Art Reports), 2011
Ricardo Lopes and Rafael Bidarra
IEEE Trans Comput Intell AI Games, 2011
Krzysztof Templin, Piotr Didyk, Tobias Ritschel, Elmar Eisemann, Karol Myszkowski, and Hans-Peter Seidel
In Proceedings of SCC, 2011