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Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Summary

The Contour Tree of a scalar field is the graph obtained by contracting all the con- nected components of the level sets of the field into points. This is a powerful ab- straction for representing the structure of the field with explicit description of the topological changes of its level sets. It has proven effective as a data-structure for fast extraction of isosurfaces and its application has been advocated as a user inter- face component guiding interactive data exploration sessions. In practice, this use has been limited to trivial examples due to the problem of presenting a graph that may be overwhelming in size and in which a planar embedding may have self-intersections. We propose a new metaphor for visualizing the Contour Tree borrowed from the classical design of a mechanical orrery – see Fig. 1a – reproducing a hierarchy of orbits of the planets around the sun or moons around a planet. In the toporrery – see Fig. 1b – the hierarchy of stars, planets and moons is replaced with a hierarchy of maxima, minima and saddles that can be interactively filtered, both uniformly and adaptively, by importance with respect to a given metric.

The implementation of the system is based on (1) a hierarchical graph model al- lowing coarse-to-fine traversal for selective refinements and (2) a new algorithm for constructing a multiresolution Contour Tree with guaranteed topological correctness independently of the simplification metric. We have tested the approach using topo- logical persistence as the main metric for constructing the tree hierarchy, and using geometric position as a secondary metric for adaptive refinements. The result is pre- sented in linked views of the abstract toporrery and the geometric embedding of the input data.

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Pascucci, V., Cole-McLaughlin, K., Scorzelli, G. (2009). The TOPORRERY: computation and presentation of multi-resolution topology. In: Möller, T., Hamann, B., Russell, R.D. (eds) Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/b106657_2

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