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改进的sift特征匹配方法.docx

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改进的sift特征匹配方法 Abstract SIFT(Scale-InvariantFeatureTransform)isapopularalgorithmusedinimageprocessingforfeaturematchingandobjectrecognition.However,thestandardSIFTalgorithmhascertainlimitations,suchastheinabilitytohandleimageswithblurryedgesorlargescalechanges.Inthisresearchpaper,weproposeanewandimprovedSIFTfeaturematchingalgorithmthatovercomestheselimitations.OurproposedalgorithmcombinestheclassicSIFTalgorithmwithanewedgedetectionalgorithmandarobustscaleestimationmethod.Bydoingso,ouralgorithmachievesmoreaccurateandrobustfeaturematchingthanthestandardSIFTalgorithm. Introduction SIFTisapowerfulalgorithmforimagefeaturematchingandobjectrecognitionthathasbeenwidelyadoptedinvariouscomputervisionapplications.Itworksbydetectinganddescribingkeyfeaturesinanimageusingascale-spacerepresentation.Thesekeyfeatures,knownaskeypoints,arerobusttoscalechanges,rotation,andilluminationchanges.TheSIFTalgorithmthenmatchesthekeypointsoftwoimagestodeterminetheirsimilarity. WhiletheSIFTalgorithmiseffectiveinmanyscenarios,ithasitslimitations.Oneofthebiggestlimitationsisitsinabilitytohandleimageswithblurryedgesorlargescalechanges.Additionally,itcanbesensitivetonoiseandoutliers,leadingtofalsematches.Therefore,thereisaneedforanimprovedSIFTfeaturematchingalgorithmthatcanovercometheselimitations. ProposedAlgorithm OurproposedalgorithmbuildsupontheclassicSIFTalgorithmbyincorporatingtwoadditionalsteps-edgedetectionandrobustscaleestimation. Step1:EdgeDetection Thefirststepinouralgorithmistodetectedgesintheimage.Thisisachievedbyusinganewedgedetectionalgorithmthatismorerobusttoimagenoiseandblurthantraditionaledgedetectionmethods,suchasCannyedgedetection.TheproposedalgorithmusesacombinationofLaplacianandGaussianfilterstodetectedgesintheimage.TheLaplacianfilterisusedtofindareasofhighchangeinintensity,whiletheGaussianfilterisusedtosmooththeimageandreducetheimpactofnoise. Step2:RobustScaleEstimation Thesecondstepinouralgorithmistoestimatethescaleofthekeypointsaccurately.ThestandardSIFTalgorithmusesthediffer