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2012InternationalConferenceonControlEngineeringandCommunicationTechnologyImagematchingcombineSIFTwithregionalSSDAQiuWentao1,2,ZhaoJian1*,LiuJie1(1ChangchunInstituteofOptics,FineMechanicsandPhysics,ChineseAcademyofSciences,Changchun130033,China,E-mail:zhaojian6789@yahoo.com;2GraduateUniversityoftheChineseAcademyofSciences,Beijing100039,China)Abstract—Imagematchingisatthebaseofmanycomputervisionproblems,suchasobjectrecognitionorimagestitching.StandardSIFTprovidespoorperformancewhenimagesunderviewpointchangeconditionsandwithsimilarcorners.Hence,weproposeamatchingalgorithmcombineregionalSSDAwithsimplifiedSIFTalgorithm.WedemonstratethroughexperimentsthatouralgorithmyieldsbetterperformanceinimagesofFigure1SIFTcomputationalprocessviewpointchangeandsimilarfeaturepoints.Besides,thesimplifiedalgorithmcutdownabouthalfthetimewasoriginallyScale-space[7]extremadetection,extremumdetectioninneededinourtestedimages.bothscalespaceandimagecoordinateplane.AGaussianKeywords-Imagematching˗SIFT˗SSDApyramidisconstructedandcandidatepointsareextractedbyscanninglocalextremuminaseriesofDoGI.INTRODUCTION(DifferenceofGaussian)images.Keypointlocation,candidatepointsarelocalizedtosub-Imagematchingisanessentialpartofmanymodernpixelaccuracythroughfittingthree-dimensionquadraticcomputervisionsystemsthatsolvetaskssuchasobjectorfunction,andunstablepointsoflowcontrastorstrongscenerecognition,stereocorrespondence,imageindexingandedgeresponseareeliminated.itisthebasisofimagestitching,imagefusion[1,2].ItisaOrientationassignment,orientationsareassignedtoeachtechniquethatwildlyusedinprocessingmedicalimages,keypointlocationbasedonstatisticalresultoflocalremotesensingimagesetc.Imageregistrationistofindaimagegradientdirections.transformationbetweentwoortwomoreimages,whichisofKeypointdescriptor,SIFTdescriptoristherepresentationthesamesceneobtainedfromdifferenttimeordifferentofstatisticallocalimagegradientswhichmeasuredatthesensorsordifferentviewpoint,enablestheiridentica
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