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1、AGeneralFrameworkforObjectDetectionConstantineP.PapageorgiouMichaelOrenTomasoPoggioCenterforBiologicalandComputationalLearningArtificialIntelligenceLaboratoryMITCambridge,MA02139{cpapa,oren,tp}@ai.mit,eduAbstractintheimage;MAPormaximumlikelihoodmethod
2、swillnotworksincetheclassificationofeachpatternThispaperpresentsageneraltrainableframeworkinanimageisdoneindependently.Thispaperalsoforobjectdetectioninstaticimagesofclutteredscenes.introducesanextensionthatusesmotioncuestoim-Thedetectiontechniquewede
3、velopisbasedonaprovedetectionaccuracyovervideosequences.Thiswaveletrepresentationofanobjectclassderivedfromamotionmoduleisageneralonethatcanbeusedwithstatisticalanalysisoftheclassinstances.Bylearningmanydetectionalgorithmsanddoesnotcompromiseanobjectc
4、lassintermsofasubsetofanovercompletetheabilityofthesystemtodetectnon-movingobjects.dictionaryofwaveletbasisfunctions,wederiveacom-Initialworkonthedetectionofrigidobjectsinpactrepresentationofanobjectclasswhichisusedasstaticimages,suchasstreetsignsorfa
5、ces,Betke&aninputtoasupporivectormachineclassifier.ThisMakris[l],Yuille,et.al.[2l],usedtemplatematch-representationovercomesboththeproblemofin-classingapproacheswithasetofrigidtemplatesorhand-variabilityandprovidesalowfalsedetectionrateincraftedparame
6、terizedcurves.Theseapproachesareunconstrainedenvironments.difficulttoextendtomorecomplexobjectssuchasWedemonstratethecapabilitiesofthetechniqueinpeople,sincetheyinvolveasignificantamountofpriortwodomainswhoseinherentinformationcontentdif-informationan
7、ddomainknowledge.Inrecentre-ferssignificantly.Thefirstsystemisfacedetectionsearch,morecloselyrelatedtooursystem,thedetec-andthesecondisthedomainofpeoplewhich,incon-tionproblemissolvedusinglearning-basedtechniquestrasttofaces,varygreatlyincolor,texture
8、,andpat-thataredatadriven.ThisapproachwasusedbySungterns.Unlikepreviousapproaches,thissystemlearns&Poggio[lG]andVaillant,etal.[l8]forthedetectionfromexamplesanddoesnotrelyonanyapriori(hand-offrontalfacesinclutteredscenes,withsimilararcbi-craft
