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1、Multi-ViewActiveShapeModelwithRobustParameterEstimationLiZhang,HaizhouAiDepartmentofComputerScienceandTechnology,TsinghuaUniversity,Beijing,100084,Chinaahz@mail.tsinghua.edu.cnAbstracttolocalizeobjectswithlargeviewchanges,especiallyfortheapplicationofmulti-viewfacealignment.TheseworksActi
2、veShapeModelisanefficientwayforlocalizingob-ofteninvolvemultipleshapeandtexturemodelsbuiltbyjectswithvariableshapes.WhenASMisextendedtomulti-non-linearmethodssuchasGaussianmixtures[1]andKer-viewcases,theparameterestimationapproachesinprevi-nelPCA[6],whileself-occlusioncanalsobehandledbyous
3、worksareoftensensitivetotheinitialview,astheydolearningvisibilityfromtrainingdata[11].However,sincenothandletheunreliabilityoflocaltexturesearch,whichthetexturemodelusedinthelocalsearchofeachlabelpointcanbecausedbybadinitializationorclutteredbackground.dependsontheview,thesemethodsareofte
4、nverysensi-Toovercomethisproblem,weproposeanovelalgorithmtivetotheestimationofthehiddenviewparameter.Whenforparameterestimation,usingrobustestimatorstoremovetheinitialviewisnotpredictedcorrect,theresultsoflo-outliers.Byweightingdynamically,ourmethodactsasacalsearchbecomeunreliable.Ifthees
5、timationoftheshapemodelselectionmethod,whichrevealsthehiddenshapeparameterdoesnotdealwiththepotentialoutliers,theseandviewparametersfromnoisyobservationsoflocaltex-multi-viewASMapproacheswillfail.turemodels.Experimentsandcomparisonsonmulti-viewToovercomethisproblem,weproposeanovelparame-f
6、acealignmentarecarriedouttoshowtheefficiencyofourterestimationapproachformulti-viewActiveShapeModel.approach.Inourmethod,everylabelpointisweighteddynamically.Onlythelabelpointsthatareconsistentwiththeshapemodelwillhavelargeweights,whileoutliershavelittle1.Introductionweightsandtheirinfluenc
7、eareeliminated.Bydoingthis,thealgorithmactsasamodelselectionmethodrevealingthehiddenviewparameterfromunreliableobservationsofActiveShapeModel(ASM)introducedbyCootes[2]labelpoints.Sinceourmethoddoesnotcompletelydependisapopularmethodforobjectregistrationinimages.Byon
