使用广义尺度空间兴趣点进行图像匹配【中文7000字】
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编号：9693822
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上传时间：2018-03-21
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使用广义尺度空间兴趣点进行图像匹配【中文7000字】,使用,广义,尺度,空间,兴趣,兴致,进行,图像,图象,匹配,中文
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IMAGEMATCHINGUSINGGENERALIZEDSCALESPACEINTERESTPOINTSTONYLINDEBERGSTARSCHOOLOFCOMPUTERSCIENCEANDCOMMUNICATIONKTHROYALINSTITUTEOFTECHNOLOGY,STOCKHOLM,SWEDENABSTRACTTHEPERFORMANCEOFMATCHINGANDOBJECTRECOGNITIONMETHODSBASEDONINTERESTPOINTSDEPENDSONBOTHTHEPROPERTIESOFTHEUNDERLYINGINTERESTPOINTSANDTHEASSOCIATEDIMAGEDESCRIPTORSTHISPAPERDEMONSTRATESTHEADVANTAGESOFUSINGGENERALIZEDSCALESPACEINTERESTPOINTDETECTORSWHENCOMPUTINGIMAGEDESCRIPTORSFORIMAGEBASEDMATCHINGTHESEGENERALIZEDSCALESPACEINTERESTPOINTSAREBASEDONLINKINGOFIMAGEFEATURESOVERSCALEANDSCALESELECTIONBYWEIGHTEDAVERAGINGALONGFEATURETRAJECTORIESOVERSCALEANDALLOWFORAHIGHERRATIOOFCORRECTMATCHESANDALOWERRATIOOFFALSEMATCHESCOMPAREDTOPREVIOUSLYKNOWNINTERESTPOINTDETECTORSWITHINTHESAMECLASSSPECIFICALLY,ITISSHOWNHOWASIGNIFICANTINCREASEINMATCHINGPERFORMANCECANBEOBTAINEDINRELATIONTOTHEUNDERLYINGINTERESTPOINTDETECTORSINTHESIFTANDTHESURFOPERATORSWEPROPOSETHATTHESEGENERALIZEDSCALESPACEINTERESTPOINTSWHENACCOMPANIEDBYASSOCIATEDSCALEINVARIANTIMAGEDESCRIPTORSSHOULDALLOWFORBETTERPERFORMANCEOFINTERESTPOINTBASEDMETHODSFORIMAGEBASEDMATCHING,OBJECTRECOGNITIONANDRELATEDVISIONTASKSKEYWORDSINTERESTPOINTS,SCALESELECTION,SCALELINKING,MATCHING,OBJECTRECOGNITION,FEATUREDETECTION,SCALEINVARIANCE,SCALESPACE1INTRODUCTIONACOMMONAPPROACHTOIMAGEBASEDMATCHINGCONSISTSOFDETECTINGINTERESTPOINTSWITHASSOCIATEDIMAGEDESCRIPTORSFROMIMAGEDATAANDTHENESTABLISHINGACORRESPONDENCEBETWEENTHEIMAGEDESCRIPTORSSPECIFICALLY,THESIFTOPERATOR1ANDTHESURFOPERATOR2HAVEBEENDEMONSTRATEDTOBEHIGHLYUSEFULFORTHISPURPOSEWITHMANYSUCCESSFULAPPLICATIONS,INCLUDINGOBJECTRECOGNITION,3DOBJECTANDSCENEMODELLING,VIDEOTRACKING,GESTURERECOGNITION,PANORAMASTITCHINGASWELLASROBOTLOCALIZATIONANDMAPPINGINTHESIFTOPERATOR,THEINTITIALDETECTIONOFINTERESTPOINTSISBASEDONDIERENCESOFGAUSSIANSFROMWHICHLOCALEXTREMAOVERSPACEANDSCALEARECOMPUTEDSUCHPOINTSAREREFERREDTOASSCALESPACEEXTREMATHEDIERENCEOFGAUSSIANOPERATORCANBESEENASANAPPROXIMATIONOFTHELAPLACIANOPERATOR,STARTHESUPPORTFROMTHESWEDISHRESEARCHCOUNCILCONTRACT20104766,THEROYALSWEDISHACADEMYOFSCIENCESANDTHEKNUTANDALICEWALLENBERGFOUNDATIONISGRATEFULLYACKNOWLEDGEDAKUIJPERETALEDSSSVM2013,LNCS7893,PP355367,2013CSPRINGERVERLAGBERLINHEIDELBERG2013356TLINDEBERGANDITFOLLOWSFROMGENERALRESULTSIN3THATTHESCALESPACEEXTREMAOFTHELAPLACIANHAVESCALEINVARIANTPROPERTIESTHATCANBEUSEDFORNORMALIZINGLOCALIMAGEPATCHESORIMAGEDESCRIPTORSWITHRESPECTTOSCALINGTRANSFORMATIONSTHESURFOPERATORISONTHEOTHERHANDBASEDONINITIALDETECTIONOFIMAGEFEATURESTHATCANBESEENASAPPROXIMATIONSOFTHEDETERMINANTOFTHEHESSIANOPERATORWITHTHEUNDERLYINGGAUSSIANDERIVATIVESREPLACEDBYANAPPROXIMATIONINTERMSOFHAARWAVELETSFROMTHEGENERALRESULTSIN3ITFOLLOWSTHATSCALESPACEEXTREMAOFTHEDETERMINANTOFTHEHESSIANDOALSOLEADTOSCALEINVARIANTBEHAVIOUR,WHICHCANBEUSEDFOREXPLAININGTHEGOODPERFORMANCEOFTHESIFTANDSURFOPERATORSUNDERSCALINGTRANSFORMATIONSTHESUBJECTOFTHISARTICLEISTOSHOWHOWTHEPERFORMANCEOFIMAGEMATCHINGCANBEIMPROVEDBYUSINGAGENERALIZEDFRAMEWORKFORDETECTINGINTERESTPOINTSFROMSCALESPACEFEATURESINVOLVINGINEWHESSIANFEATURESTRENGTHMEASURESATAFIXEDSCALE,IILINKINGOFIMAGEFEATURESOVERSCALEINTOFEATURETRAJECTORIESTOALLOWFORABETTERSELECTIONOFSIGNIFICANTIMAGEFEATURESANDIIISCALESELECTIONBYWEIGHTEDAVERAGINGALONGFEATURETRAJECTORIESTOALLOWFORMOREROBUSTSCALEESTIMATESBYREPLACINGTHEINTERESTPOINTSINTHEREGULARSIFTANDSURFOPERATORSBYGENERALIZEDSCALESPACEINTERESTPOINTSTOBEDESCRIBEDBELOW,ITISPOSSIBLETODEFINENEWSCALEINVARIANTIMAGEDESCRIPTORSTHATLEADTOBETTERMATCHINGPERFORMANCECOMPAREDTOTHEPERFORMANCEOBTAINEDBYCORRESPONDINGINTERESTPOINTDETECTIONMECHANISMSASUSEDINTHESIFTANDSURFOPERATORS2GENERALIZEDSCALESPACEINTERESTPOINTSBASICREQUIREMENTSONTHEINTERESTPOINTSONWHICHIMAGEMATCHINGISTOBEPERFORMEDARETHATTHEYSHOULDIHAVEACLEAR,PREFERABLYMATHEMATICALLYWELLFOUNDED,DEFINITION,IIHAVEAWELLDEFINEDPOSITIONINIMAGESPACE,IIIHAVELOCALIMAGESTRUCTURESAROUNDTHEINTERESTPOINTTHATARERICHININFORMATIONCONTENTSUCHTHATTHEINTERESTPOINTSCARRYIMPORTANTINFORMATIONTOLATERSTAGESANDIVBESTABLEUNDERLOCALANDGLOBALDEFORMATIONSOFTHEIMAGEDOMAIN,INCLUDINGPERSPECTIVEIMAGEDEFORMATIONSANDILLUMINATIONVARIATIONSSUCHTHATTHEINTERESTPOINTSCANBERELIABLYCOMPUTEDWITHAHIGHDEGREEOFREPEATABILITY21DIERENTIALENTITIESFORDETECTINGSCALESPACEINTERESTPOINTSASBASISFORPERFORMINGLOCALIMAGEMEASUREMENTSONATWODIMENSIONALIMAGEF,WEWILLCONSIDERASCALESPACEREPRESENTATION410LX,YTINTEGRALDISPLAYU,VIR2FXU,YVGU,VTDUDV1GENERATEDBYCONVOLUTIONWITHGAUSSIANKERNELSGX,YT12TEX2Y2/2TOFINCREASINGWIDTH,WHERETHEVARIANCETISREFERREDTOASTHESCALEPARAMETER,ANDWITHSCALENORMALIZEDDERIVATIVESWITH1DEFINEDACCORDINGTOAT/2XANDT/2Y3TODETECTINTERESTPOINTSWITHINTHISSCALESPACEFRAMEWORK,WEWILLCONSIDERIMAGEMATCHINGUSINGGENERALIZEDSCALESPACEINTERESTPOINTS357IEITHERTHESCALENORMALIZEDLAPLACIANOPERATOR32NORMLTLXXLYY2ORTHESCALENORMALIZEDDETERMINANTOFTHEHESSIAN3DETHNORMLT2LXXLYYL2XY,3IIEITHEROFTHEFOLLOWINGDIERENTIALANALOGUES/EXTENSIONSOFTHEHARRISOPERATOR11PROPOSEDIN12,13THEUNSIGNEDHESSIANFEATURESTRENGTHMEASUREID1,NORMLBRACELEFTBIGGT2DETHLKTRACE2HLIFDETHLKTRACE2HL00OHRWIS4ORTHESIGNEDHESSIANFEATURESTRENGTHMEASUREID1,NORMLT2DETHLKTRACE2HLIFDETHLKTRACE2HL0T2DETHLKTRACE2HLIFDETHLKTRACE2HL0074840799407512075740749807784DETHNORMLD1L0077210822507635079320767808079DETHNORMLD1L0076910816307602078410764708002D1,NORML077190828007596079770765808128D1,NORML076980824107578079160763808079D2,NORMLD1L0072030818707111077760715707981D2,NORMLD1L0072040826107113077660715908014HARRISLAPLACE070020785507046075350702407695HARRISDETHESSIAN074060760807561073190740607463ECIENCYSURFLIKEIMAGEDESCRIPTORSCALINGFORESHORTENINGAVERAGEINTERESTPOINTSEXTRLINKEXTRLINKEXTRLINK2NORMLD1L0074240783207280071400735207486DETHNORMLD1L0076560807207402075040752907788DETHNORMLD1L0076280801507372074300750007723D1,NORML076610812607354075370750707831D1,NORML076400808107334074780748707779D2,NORMLD1L0071570801406870072840701307649D2,NORMLD1L0071580810006873073280701507714HARRISLAPLACE069480762006724069440683607282HARRISDETHESSIAN073450738107192067050726807043THEHARRISOPERATOR11ITHEHARRISLAPLACEOPERATOR19BASEDONSPATIALEXTREMAOFTHEHARRISMEASUREANDSCALESELECTIONFROMLOCALEXTREMAOVERSCALEOFTHESCALENORMALIZEDLAPLACIAN,IIASCALELINKEDVERSIONOFTHEHARRISLAPLACEOPERATORWITHSCALESELECTIONBYWEIGHTEDAVERAGINGOVERFEATURETRAJECTORIESOFHARRISFEATURES12,ANDIIIIVTWOHARRISDETHESSIANOPERATORSANALOGOUSTOTHEHARRISLAPLACEOPERATORS,WITHTHEDIERENCETHATSCALESELECTIONISPERFORMEDBASEDONTHESCALENORMALIZEDDETERMINANTOFTHEHESSIANINSTEADOFTHESCALENORMALIZEDLAPLACIAN12IMAGEMATCHINGUSINGGENERALIZEDSCALESPACEINTERESTPOINTS363TABLE2THEFIVEBESTCOMBINATIONSOFINTERESTPOINTSANDIMAGEDESCRIPTORSAMONGTHE22936COMBINATIONSCONSIDEREDINTHISEXPERIMENTALEVALUATIONASRANKEDONTHERATIOOFINTERESTPOINTSTHATLEADTOCORRECTMATCHESFORCOMPARISON,RESULTSAREALSOSHOWNFORTHESIFTDESCRIPTORBASEDONSCALESPACEEXTREMAOFTHELAPLACIAN,THESIFTORSURFDESCRIPTORSBASEDONSCALESPACEEXTREMAOFTHEDETERMINANTOFTHEHESSIANANDTHESIFTDESCRIPTORBASEDONHARRISLAPLACEINTERESTPOINTSINTERESTPOINTSANDIMAGEDESCRIPTORSRANKEDONMATCHINGECIENCYINTERESTPOINTSSCALESELECTIONDESCRIPTORECIENCYD1,NORMLLINKSIFT08128D1,NORMLLINKSIFT08079DETHNORMLD1L0LINKSIFT08079D2,NORMLD1L0LINKSIFT08014DETHNORMLD1L0LINKSIFT08002DETHNORMLD1L0EXTRSIFT07721DETHNORMLD1L0EXTRSURF076562NORMLD1L0EXTRSIFT07484HARRISLAPLACEEXTRSIFT07002THEEXPERIMENTSAREBASEDONDETECTINGTHEN800STRONGESTINTERESTPOINTSEXTRACTEDFROMTHEFIRSTIMAGE,REGARDEDASREFERENCEIMAGEFORTHEHOMOGRAPHYTOOBTAINANAPPROXIMATEUNIFORMDENSITYOFINTERESTPOINTSUNDERSCALINGTRANSFORMATIONS,ANADAPTEDNUMBERNPRIMEN/S2OFINTERESTPOINTSISSEARCHEDFORIWITHINTHESUBWINDOWOFTHEREFERENCEIMAGETHATISMAPPEDTOTHEINTERIOROFTHETRANSFORMEDIMAGEANDIIINTHETRANSFORMEDIMAGE,WITHSDENOTINGRELATIVESCALINGFACTORBETWEENTHETWOIMAGESTHISPROCEDUREISREPEATEDFORALLPAIRSOFIMAGESWITHINTHEGROUPSOFDISTANCEVARIATIONSORVIEWINGVARIATIONSRESPECTIVELY,IMPLYINGUPTO55IMAGEPAIRSFORTHESCALINGTRANSFORMATIONSAND6IMAGEPAIRSFORTHEFORESHORTENINGTRANSFORMATIONS,IEUPTO61MATCHINGEXPERIMENTSFOREACHONEOFTHE12POSTERS,THUSUPTO732EXPERIMENTSFOREACHONEOF29INTERESTPOINTDETECTORSASCANBESEENFROMTHERESULTSOFMATCHINGSIFTORSURFLIKEIMAGEDESCRIPTORSINTABLE1,THEINTERESTPOINTDETECTORSBASEDONSCALELINKINGANDWITHSCALESELECTIONBYWEIGHTEDAVERAGINGALONGFEATURETRAJECTORIESGENERALLYLEADTOSIGNIFICANTLYHIGHERECIENCYRATESCOMPAREDTOTHECORRESPONDINGINTERESTPOINTDETECTORSBASEDONSCALESELECTIONFROMLOCALEXTREMAOVERSCALESPECIFICALLY,THEHIGHESTECIENCYRATESAREOBTAINEDWITHTHESCALELINKEDVERSIONOFTHEUNSIGNEDHESSIANFEATURESTRENGTHMEASURED1,NORML,FOLLOWEDBYSCALELINKEDVERSIONSOFTHEUNSIGNEDSIGNEDHESSIANFEATURESTRENGTHMEASURED1,NORMLANDTHEDETERMINANTOFTHEHESSIANOPERATORDETHNORMLWITHCOMPLEMENTARYTHRESHOLDINGOND1,NORML0364TLINDEBERGSCALINGVARIATIONSFORESHORTENINGVARIATIONS00511522530550606507075080852LDETHLD1L0DETHLD1L0D1LD1LD2LD1L0HARRISLAPLACE2LEXTR2025303540450550606507075080852LDETHLD1L0DETHLD1L0D1LD1LD2LD1L0HARRISLAPLACE2LEXTRFIG3GRAPHSSHOWINGHOWTHEMATCHINGECIENCYDEPENDSUPONLEFTTHEAMOUNTOFSCALINGS125,60FORSCALINGTRANSFORMATIONSWITHLOG2SONTHEHORIZONTALAXISANDRIGHTTHEDIERENCEINVIEWINGANGLE225,45FORTHEFORESHORTENINGTRANSFORMATIONSFORINTERESTPOINTMATCHINGBASEDONSIFTLIKEIMAGEDESCRIPTORSCORRESPONDINGEXPERIMENTALRESULTSTHATCANNOTBEINCLUDEDHEREBECAUSEOFLACKOFSPACESHOWTHATTHELOWESTANDTHUSTHEBEST1PRECISIONSCOREISOBTAINEDWITHTHEDETERMINANTOFTHEHESSIANOPERATORDETHNORMLWITHCOMPLEMENTARYTHRESHOLDINGOND1,NORML0,FOLLOWEDBYTHEDETERMINANTOFTHEHESSIANOPERATORDETHNORMLWITHCOMPLEMENTARYTHRESHOLDINGOND1,NORML0AMONGTHEMORETRADITIONALFEATUREDETECTORSBASEDONSCALESELECTIONFROMLOCALEXTREMAOVERSCALE,WECANALSONOTETHATTHEDETERMINANTOFTHEHESSIANOPERATORDETHNORMLPERFORMSSIGNIFICANTLYBETTERTHANBOTHTHELAPLACIANOPERATOR2NORMLANDTHEHARRISLAPLACEOPERATORWECANALSONOTETHATTHEHARRISLAPLACEOPERATORCANBEIMPROVEDBYEITHERSCALELINKINGORBYREPLACINGSCALESELECTIONBASEDONTHESCALENORMALIZEDLAPLACIANBYSCALESELECTIONBASEDONTHESCALENORMALIZEDDETERMINANTOFTHEHESSIANWHENCOMPARINGTHERESULTSOBTAINEDFORSIFTLIKEANDSURFLIKEIMAGEDESCRIPTORS,WECANSEETHATTHESIFTLIKEIMAGEDESCRIPTORSLEADTOBOTHHIGHERECIENCYRATESANDLOWER1PRECISIONSCORESTHANTHESURFLIKEIMAGEDESCRIPTORSTHISQUALITATIVERELATIONSHIPHOLDSOVERALLTYPESOFINTERESTP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