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Related Experiment Videos

Anisotropic noise samples.

Louis Feng1, Ingrid Hotz, Bernd Hamann

  • 1Department of Computer Science, University of California, Davis, CA 95616, USA. louisfeng@gmail.com

IEEE Transactions on Visualization and Computer Graphics
|January 15, 2008
PubMed
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We developed a method to create anisotropic samples, useful for generating textures and visualizing data. This technique ensures samples are evenly distributed, reducing low-frequency noise for better visual quality.

Area of Science:

  • Computer Graphics
  • Scientific Visualization
  • Computational Geometry

Background:

  • Anisotropic samples, unlike isotropic ones, are non-overlapping ellipses matching a specific metric.
  • These samples are valuable for texture generation (e.g., line integral convolution) and direct visualization.
  • Metric tensors are key for defining anisotropic samples, making them suitable for visualizing tensor fields.

Purpose of the Study:

  • To present a practical method for generating stochastic anisotropic samples with Poisson-disk characteristics.
  • To enable the creation of non-overlapping elliptical samples whose size and density conform to a given anisotropic metric.
  • To provide a technique applicable to visualization and graphics, particularly for tensor field representation.

Main Methods:

Related Experiment Videos

  • Combines sampling theory and mesh generation principles.
  • Constructs an initial set of non-overlapping ellipses matching the metric.
  • Applies a generalized anisotropic Lloyd relaxation using a discrete Voronoi cell and centroid computation.
  • Utilizes automatic packing for elliptical samples.

    • Main Results:

    • Generated anisotropic samples with desired properties and even distribution.
    • Produced textures comparable to those from anisotropic reaction-diffusion methods.
    • Achieved samples exhibiting blue noise properties by minimizing low frequencies in the power spectrum.
    • Validated sample uniformity using Fourier analysis.

    Conclusions:

    • The presented method offers a practical approach to generating high-quality anisotropic samples.
    • The technique is well-suited for applications requiring metric-based sampling, such as tensor field visualization.
    • The resulting samples possess desirable properties, including blue noise characteristics, enhancing their utility in computer graphics and visualization.