Summary
We present a simple and efficient approach to generate a dense set of anisotropic, spatially varying glyphs over a two-dimensional domain. Such glyph samples are useful for many visualization and graphics applications. The glyphs are embedded in a set of nonoverlapping ellipses whose size and density match a given anisotropic metric. An additional parameter controls the arrangement of the ellipses on lines, which can be favorable for some applications, for example, vector fields and distracting for others. To generate samples with the desired properties, we combine ideas from sampling theory and mesh generation. We start with constructing a first set of nonoverlapping ellipses whose distribution closely matches the underlying metric. This set of samples is used as input for a generalized anisotropic Lloyd relaxation to distribute samples more evenly.
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Acknowledgments
The brain dataset is the courtesy of Gordon Kindlmann, Scientific Computing and Imaging Institute, University of Utah, and Andrew Alexander, W. M. Keck Laboratory for Functional Brain Imaging and Behavior, University of Wisconsin-Madison. This work was partially supported by the German Research Foundation DFG (Emmy-Noether Research group) and by the National Science Foundation under contracts ACI 9624034 (CAREER Award), through the Large Scientific and Software Data Set Visualization (LSSDSV) program under contract ACI 9982251, and a large Information Technology Research (ITR) grant; the National Institutes of Health under contract P20 MH60975-06A2, funded by the National Institute of Mental Health and the National Science Foundation. Further, we thank the members of the Visualization and Computer Graphics Research Groups at the Zuse Institute Berlin and the Institute for Data Analysis and Visualization (IDAV) at the University of California, Davis.
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Feng, L., Hotz, I., Hamann, B., Joy, K. (2009). Dense Glyph Sampling for Visualization. In: Laidlaw, D., Weickert, J. (eds) Visualization and Processing of Tensor Fields. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88378-4_9
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DOI: https://doi.org/10.1007/978-3-540-88378-4_9
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