AHD: The alternate hierarchical decomposition of nonconvex polytopes (generalization of a convex polytope based spatial data model)

Rizwan Bulbul*, Andrew U. Frank

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Robust convex decomposition, RCD, of polytopes is the convex decomposition of nonconvex polytopes using algorithms whose implementation is based on arbitrary precision arithmetic. Decomposing nonconvex polytopes using RCD can make the data representation model consistent enabling generalization with level of detail. Our approach, alternate hierarchical decomposition, AHD, for the decomposition of nonconvex polytopes with arbitrary genus, is a recursive approach whose implementation is robust, efficient and scalable to any dimension. Our approach decomposes the given nonconvex polytope with arbitrary genus into a set of component convex hulls, which are represented hierarchically in a tree structure, convex hull tree, CHT.
Original languageEnglish
Title of host publication2009 17th International Conference on Geoinformatics
Pages1-6
DOIs
Publication statusPublished - 12 Aug 2009
Externally publishedYes
Event17th International Conference on Geoinformatics - CSISS, George Mason University, Fairfax, United States
Duration: 12 Aug 200914 Aug 2009
https://ieeexplore.ieee.org/xpl/conhome/5286203/proceeding

Conference

Conference17th International Conference on Geoinformatics
Abbreviated titlegeoinfotmatics2009
CountryUnited States
CityFairfax
Period12/08/0914/08/09
Internet address

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