Section: New Results
Visualization of Scientific Data
Hierarchical segmentation of shapes based on curvature
This project is part of a collaboration with IDAV ( http://www.idav.ucdavis.edu/ ). A high-level approach to describe the characteristics of a surface is to segment it into regions of uniform curvature behavior and construct an abstract representation given by a (topology) graph. We propose a surface segmentation method based on discrete mean and Gaussian curvature estimates. The surfaces are obtained from three-dimensional imaging data sets by isosurface extraction after data presmoothing and postprocessing the isosurfaces by a surface-growing algorithm. We generate a hierarchical multiresolution representation of the isosurface. Segmentation and graph generation algorithms can be performed at various levels of detail. At a coarse level of detail, the algorithm detects the main features of the surface. This low-resolution description is used to determine constraints for the segmentation and graph generation at the higher resolutions. We have applied our methods to MRI data sets of human brains. The hierarchical segmentation framework can be used for brain-mapping purposes. These results have been published in  .
Visualisation of large numerical simulation data sets
This project is part of a collaboration with CEA/CESTA. CEA/CESTA has to perform numerical simulation on very large data sets, in thermodynamics, mechanics, aerodynamics, neutronics, etc. Visualization of the results of these simulations is crucial in order to gain understanding of the phenomena that are simulated. The visualization techniques need to be interactive - if not real time, to be helpful for engineers. Therefore multiresolution techniques are required to accelerate the visual exploration of the data sets. We are developing multiresolution algorithms devoted to specific type of data sets. Our current focus is on volumetric data sets based on tetrahedral grids in which inner structures of dimension 2, 1 or 0 must be preserved, both geometrically and topologically. To maintain these important features during the multiresolution decomposition, techniques based on combinatorial topology have been developed. The first results concern the preservation of 1D and 0D structures in triangular grids. Figure 8 illustrates the preservation of polylines (in red) and specific points (in yellow) in a CAD/CAM mesh. The generalization of these results to the volumetric case has been developed, and is currently submitted for publication.
This project is part of a collaboration with the research and development department of EDF, and with LPPA (Laboratoire de Physiologie de la Perception et de l'Action, Collège de France). The general context is similar to the collaboration with CEA (Section 6.3.2 ), i.e. the visualisation of large numerical data sets. The focus in this project in on the following problem: How should human perception be taken into account in Visualization algorithms, and more specifically in algorithms based on multiresolution techniques. Previous works in this area are mostly based on image analysis techniques, that are used to measure important features in a static image resulting from some visualization algorithm. These results do not take into account information on the specific person using the visualization system. We are especially interested in taking into account such information, like for example the point where the user is looking at. Also we want to insert dynamic parameters in the perceptive measure, like the movement of the user's head, since such parameters greatly influence the actual perception of the rendered scene. In the framework of this collaboration, EDF is funding a PhD grant on these topics, started by Christian Boucheny in December 2005.