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1、AGlobalSamplingMethodforAlphaMattingKaimingHe1,ChristophRhemann2,CarstenRother3,XiaoouTang1,JianSun412TheChineseUniversityofHongKong,ViennaUniversityofTechnology,34MicrosoftResearchCambridge,MicrosoftResearchAsiaAbstractExistingmattingmethodscanbecategorizedaspropagation-based
2、orsampling-based.Propagation-basedAlphamattingreferstotheproblemofsoftlyextractingmethodstreattheproblemasinterpolatingtheunknownal-theforegroundfromanimage.Givenatrimap(specify-phavaluesfromtheknownregions.Theinterpolationcaningknownforeground/backgroundandunknownpixels),abed
3、onebysolvinganaffinitymatrix[17,8,11,16,9],bystraightforwardwaytocomputethealphavalueistosam-optimizingMarkovRandomFields[18],orbycomputingplesomeknownforegroundandbackgroundcolorsforeachgeodesicdistance[2].Thesemethodsmainlyrelyontheunknownpixel.Existingsampling-basedmattingme
4、thodsimage’scontinuitytoestimatethealphamatte,anddonotoftencollectsamplesneartheunknownpixelsonly.Theyexplicitlyaccountfortheforegroundandbackgroundcol-failifgoodsamplescannotbefoundnearby.ors.Theyhaveshownsuccessinmanycases,butmayfailInthispaper,weproposeaglobalsamplingmethod
5、thatwhentheforegroundhaslongandthinstructuresorholes.usesallsamplesavailableintheimage.Ourglobalsam-Theirperformancecanbeimprovedwhencombinedwithplesetavoidsmissinggoodsamples.Asimplebutef-sampling-basedmethods(e.g.,[19,12,7]).fectivecostfunctionisdefinedtotackletheambiguityinS
6、ampling-basedmethodsfirstestimatetheforegroundthesampleselectionprocess.Tohandlethecomputationalandbackgroundcolorsandthencomputethealphamatte.complexityintroducedbythelargenumberofsamples,weEarliermethodslikeRuzonandTomasi’swork[15]andposethesamplingtaskasacorrespondenceproble
7、m.TheBayesianMatting[6],fitaparametricmodeltothecol-correspondencesearchisefficientlyachievedbygeneraliz-ordistributions.Buttheyarelessvalidwhentheimageingarandomizedalgorithmpreviouslydesignedforpatchdoesnotsatisfythemodel.Recentsampling-basedmeth-matching[3].Avarietyofexperime
8、ntsshowthatourglobalods[19,12,7]aremostlynon-parametric:theyp