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Functional Decomposition for Bundled Simplification of Trail Sets

Abstract : Bundling visually aggregates curves to reduce clutter and help finding important patterns in trail-sets or graph drawings. We propose a new approach to bundling based on functional decomposition of the underling dataset. We recover the functional nature of the curves by representing them as linear combinations of piecewise-polynomial basis functions with associated expansion coefficients. Next, we express all curves in a given cluster in terms of a centroid curve and a complementary term, via a set of so-called principal component functions. Based on the above, we propose a two-fold contribution: First, we use cluster centroids to design a new bundling method for 2D and 3D curve-sets. Secondly, we deform the cluster centroids and generate new curves along them, which enables us to modify the underlying data in a statistically-controlled way via its simplified (bundled) view. We demonstrate our method by applications on real-world 2D and 3D datasets for graph bundling, trajectory analysis, and vector field and tensor field visualization.
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Soumis le : vendredi 29 septembre 2017 - 23:48:46
Dernière modification le : mercredi 3 novembre 2021 - 05:38:22
Archivage à long terme le : : samedi 30 décembre 2017 - 16:00:57


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Christophe Hurter, Stéphane Puechmorel, Florence Nicol, Alexandru C Telea. Functional Decomposition for Bundled Simplification of Trail Sets. IEEE Transactions on Visualization and Computer Graphics, 2017, PP (99), pp.1 - 1. ⟨10.1109/TVCG.2017.2744338⟩. ⟨hal-01587221⟩



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