The goal of this project is to generate stochastic anisotropic samples with Poisson-disk characteristic over a two-dimensional domain. In contrast to isotropic samples, we understand anisotropic samples as non-overlapping ellipses whose size and density match a given anisotropic metric. Anisotropic noise samples are useful for many visualization and graphics applications. The spot samples can be used as input for texture generation, e.g., line integral convolution (LIC), but can also be used directly for visualization. The definition of the spot samples by a metric tensor makes them especially suitable for the visualization of tensor fields that can be translated into a metric. Our work combines ideas from sampling theory and mesh generation.
DFG Emmy Noether Nachwuchsgruppe Vergleichende Visualisierung (09/2008–09/2011)
- Andrea Kratz, Nino Kettlitz, Ingrid Hotz. Particle-Based Anisotropic Sampling for Two-Dimensional Tensor Field Visualization. Vision Modeling and Visualization (VMV'11), pp. 145-152, 2011.
- Louis Feng, Ingrid Hotz, Bernd Hamann, Kenneth Joy. Anisotropic Noise Samples for Tensor Field Visualization. IEEE Transactions on Visualization and Computer Graphics. pp. 342-354, 2008
- Louis Feng, Ingrid Hotz, Bernd Hamann, Kenneth I. Joy. Dense Glyph Sampling for Visualization. Visualization and Processing of Tensor Fields: Advances and Perspectives. Springer-Verlag, pp. 177-193. 2008.