Anisotropic noise samples

IEEE Transactions on Visualization and Computer Graphics
Louis FengKenneth Joy

Abstract

We present a practical approach 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 using 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. To generate these samples with the desired properties we construct a first set of non-overlapping ellipses whose distribution closely matches the underlying metric. This set of samples is used as input for a generalized anisotropic Lloyd relaxation to distribute noise samples more evenly. Instead of computing the Voronoi tessellation explicitly, we introduce a discrete approach which combines the Voronoi cell and centroid computation in one step. Our method supports automatic packin...Continue Reading

References

Nov 4, 2006·Pharmaceutical Statistics·Yinghui ZhouJohn W Stevens

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Citations

Jul 13, 2013·IEEE Transactions on Visualization and Computer Graphics·Xiang YingYing He
Sep 14, 2013·IEEE Transactions on Visualization and Computer Graphics·Andrea KratzIngrid Hotz
May 11, 2013·IEEE Transactions on Visualization and Computer Graphics·Jonathan A QuinnRalph R Martin
Oct 29, 2011·IEEE Transactions on Visualization and Computer Graphics·Guoning ChenEugene Zhang
Oct 27, 2010·IEEE Transactions on Visualization and Computer Graphics·Thomas Schultz, Gordon L Kindlmann
Sep 26, 2014·International Journal of Biomedical Imaging·Mark HöllerHans-H Ehricke

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