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结合形状先验的图割目标分割方法.docx

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结合形状先验的图割目标分割方法 Title:ShapePriorGuidedGraphCutforImageSegmentation Abstract: Imagesegmentationplaysacrucialroleinnumerouscomputervisionapplications,suchasobjectrecognition,sceneunderstanding,andmedicalimageanalysis.However,accurateandefficientsegmentationremainschallengingduetothecomplexityanddiversityofimagecontent.Inthispaper,weproposeanovelimagesegmentationmethod,namelyShapePriorGuidedGraphCut,whichleveragestheshapepriorknowledgetoimprovetheaccuracyandrobustnessofthesegmentationresults.Theproposedmethodcombinesthepowerofgraphcutalgorithmsandshapepriorinformationtoachievesuperiorsegmentationperformance.Experimentalresultsdemonstrateitseffectivenessindifferentimagesegmentationtasks. 1.Introduction Imagesegmentationistheprocessofpartitioninganimageintocoherentregionsbasedoncertaincriteriaorconstraints.Itisafundamentaltaskincomputervisionandhasbeenextensivelyresearchedovertheyears.Traditionalsegmentationmethodsoftenrelyonlow-levelfeatures,suchascolor,texture,orgradient,whichmaynotalwayscapturethedesiredobjectboundariesaccurately.Toaddressthislimitation,incorporatinghigh-levelshapepriorinformationintothesegmentationprocesshasgainedincreasingattention. 2.RelatedWork Manypreviousstudieshaveexploredtheuseofshapepriorsforimagesegmentation.Themostcommonlyusedapproachesincludeactivecontours,levelsets,andMarkovrandomfields.Whilethesemethodshaveshownpromisingresults,theirrelianceonspecificinitializationandthehighcomputationalcomplexitylimittheirpracticalapplications.Motivatedbytheseissues,weproposeanovelmethodthatcombinestheadvantagesofgraphcutalgorithmsandshapepriorstoachievemoreaccurateandefficientimagesegmentation. 3.ShapePriorGuidedGraphCut Ourproposedmethodconsistsofthreemainsteps:shapepriorextraction,graphconstruction,andgraphcutoptimization.Firstly,theshapepriorsareextractedfromasetoftrainingimagesorlearnedfromaseparatedatasetusingtechniquessuchasprincipalcomponentanalysis(PCA)orGaussianmixturemodels(GMM).Theshapepriorscapturethetypicalobjectshapesandtheirvariations,al