摘要
Efficientdatavisualizationtechniquesarecriticalformanyscientificapplications.CentroidalVoronoitessellation(CVT)basedalgorithmsofferaconvenientvehicleforperformingimageanalysis,segmentationandcompressionwhileallowingtooptimizeretainedimagequalitywithrespecttoagivenmetric.InexperimentalsciencewithdatacountsfollowingPoissondistributions,severalCVT-baseddatatessellationalgorithmshavebeenrecentlydeveloped.Althoughtheysurpasstheirpredecessorsinrobustnessandqualityofreconstructeddata,timeconsumptionremainstobeanissueduetoheavyutilizationoftheslowlyconvergingLloyditeration.Thispaperdiscussesonepossibleapproachtoacceleratingdatavisualizationalgorithms.ItreliesonamultidimensionalgeneralizationoftheoptimizationbasedmultilevelalgorithmforthenumericalcomputationoftheCVTsintroducedin[1],wherearigorousproofofitsuniformconvergencehasbeenpresentedin1-dimensionalsetting.Themultidimensionalimplementationemploysbarycentriccoordinatebasedinterpolationandmaximalindependentsetcoarseningprocedures.Itisshownthatwhencoupledwithbinaccretionalgorithmaccountingforthediscretenatureofthedata,thealgorithmoutperformsLloyd-basedschemesandpreservesuniformconvergencewithrespecttotheproblemsize.Althoughnumericaldemonstrationsprovidedarelimitedtospectroscopydataanalysis,themethodhasacontext-independentsetupandcanpotentiallydeliversignificantspeeduptootherscientificandengineeringapplications.
出版日期
2010年02月12日(中国期刊网平台首次上网日期,不代表论文的发表时间)