Planar Minimization Diagrams via Subdivision with Applications to Anisotropic Voronoi Diagrams

Let X = {f1, …, fn} be a set of scalar functions of the form fi : ℝ2 → ℝ which satisfy some natural properties. We describe a subdivision algorithm for computing a clustered ε‐isotopic approximation of the minimization diagram of X. By exploiting soft predicates and clustering of Voronoi vertices, o...

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Bibliographic Details
Published inComputer graphics forum Vol. 35; no. 5; pp. 229 - 247
Main Authors Bennett, H., Papadopoulou, E., Yap, C.
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.08.2016
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Summary:Let X = {f1, …, fn} be a set of scalar functions of the form fi : ℝ2 → ℝ which satisfy some natural properties. We describe a subdivision algorithm for computing a clustered ε‐isotopic approximation of the minimization diagram of X. By exploiting soft predicates and clustering of Voronoi vertices, our algorithm is the first that can handle arbitrary degeneracies in X, and allow scalar functions which are piecewise smooth, and not necessarily semi‐algebraic. We apply these ideas to the computation of anisotropic Voronoi diagram of polygonal sets; this is a natural generalization of anisotropic Voronoi diagrams of point sites, which extends multiplicatively weighted Voronoi diagrams. We implement a prototype of our anisotropic algorithm and provide experimental results.
Bibliography:ArticleID:CGF12979
istex:FE3E80C9E7922B52CEDE558E09766C85AE01521A
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Chee and Huck are supported by NSF Grant #CCF‐1423228.
Evanthia is supported by SNSF #20GG21‐134355, #200021E‐154387.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12979