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dc.contributor.authorYuksel, Cemen_US
dc.contributor.editorOlga Sorkine-Hornung and Michael Wimmeren_US
dc.date.accessioned2015-04-16T07:42:56Z
dc.date.available2015-04-16T07:42:56Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.12538en_US
dc.description.abstractIn this paper we describe sample elimination for generating Poisson disk sample sets with a desired size. We introduce a greedy sample elimination algorithm that assigns a weight to each sample in a given set and eliminates the ones with greater weights in order to pick a subset of a desired size with Poisson disk property without having to specify a Poisson disk radius. This new algorithm is simple, computationally efficient, and it can work in any sampling domain, producing sample sets with more pronounced blue noise characteristics than dart throwing. Most importantly, it allows unbiased progressive (adaptive) sampling and it scales better to high dimensions than previous methods. However, it cannot guarantee maximal coverage. We provide a statistical analysis of our algorithm in 2D and higher dimensions as well as results from our tests with different example applications.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleSample Elimination for Generating Poisson Disk Sample Setsen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.sectionheadersSampling & Skinsen_US
dc.description.volume34en_US
dc.description.number2en_US
dc.identifier.doi10.1111/cgf.12538en_US
dc.identifier.pages025-032en_US


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