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基于图切割的多频带融合图像拼接.docx

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基于图切割的多频带融合图像拼接 Abstract Imagestitchingiswidelyusedincomputervisionapplications,suchaspanoramicimagecreationandobjecttracking.However,thelimitationsoftraditionalimagestitchingmethodshavebeenexposedinrecentyears.Inthispaper,weproposeamulti-bandimagefusionapproachbasedongraphsegmentationforimagestitching.Theproposedapproachcanovercomethelimitationsoftraditionalimagestitchingmethodsandcanbeusedforvariousapplications. Introduction Imagestitchingistheprocessofcombiningmultipleimagesintoalargerimage.Traditionalimagestitchingmethodstypicallyusefeature-basedapproaches,suchasSIFTandSURF,todetectandmatchkeypointsintheimages.However,thesemethodshaveseverallimitations,includingtheinabilitytohandlelarge-scalechangesinilluminationandgeometricdistortion. Inrecentyears,graph-cut-basedmethodshavebeenproposedforimagestitching.Graph-cut-basedmethodsdividetheimagesintosegmentsandoptimizethestitchingprocessusinggraph-cuts.However,thesemethodsarestillnotsufficientforhandlinglarge-scalechangesinilluminationandgeometricdistortion. Toaddressthesechallenges,weproposeamulti-bandimagefusionapproachbasedongraphsegmentationforimagestitching.Ourapproachinvolvesdividingtheimageintomultiplebandsandapplyinggraphsegmentationtoeachband.Thesegmentedimagesarethenmergedusingaweightedaverageapproach. Multi-bandImageFusionApproach Ourmulti-bandimagefusionapproachconsistsofseveralsteps: 1.ImageBandDecomposition:Wefirstdecomposetheinputimagesintomultiplebands.Eachbandcontainsadifferentfrequencyrangeoftheimage.WeuseaLaplacianpyramiddecompositionmethodtocomputethebands. 2.GraphSegmentation:Weapplygraphsegmentationtoeachband.Thegraphsegmentationmethodweuseinvolvescreatingaweightedgraphoftheimage.Thenodesofthegrapharethepixelsoftheimage,andtheedgesaretheedgesbetweenadjacentpixels.Theweightsoftheedgesarebasedonthepixelvaluesandthespatialdistancebetweenpixels. 3.OptimizeGraph-Cut:Weoptimizethegraph-cutsoneachbandusingaminimumcutalgorithm.Theminimumcutalgorithmseekstominimizethegraph-cutbyseparatingtheimageintotwop