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1、ActiveAppearanceModelsRevisitedIainMatthewsandSimonBakerTheRoboticsInstituteCarnegieMellonUniversityAbstractActiveAppearanceModels(AAMs)andthecloselyrelatedconceptsofMorphableModelsandActiveBlobsaregenerativemodelsofacertainvisualphenomenon.Althoughlinearinboths
2、hapeandappearance,overall,AAMsarenonlinearparametricmodelsintermsofthepixelintensities.FittinganAAMtoanimageconsistsofminimisingtheerrorbetweentheinputimageandtheclos-estmodelinstance;i.e.solvinganonlinearoptimisationproblem.Weproposeanefficientfittingalgorithmfor
3、AAMsbasedontheinversecompositionalimagealignmentalgorithm.Weshowthattheeffectsofappearancevariationduringfittingcanbeprecomputed(“projectedout”)usingthisalgorithmandhowitcanbeextendedtoincludeaglobalshapenormalisingwarp,typicallya2Dsimilaritytransformation.Weeval
4、uateouralgorithmtodeterminewhichofitsnovelaspectsimproveAAMfittingperformance.Keywords:ActiveAppearanceModels,AAMs,ActiveBlobs,MorphableModels,fitting,effi-ciency,Gauss-Newtongradientdescent,inversecompositionalimagealignment.1IntroductionActiveAppearanceModels(AAM
5、s)[7–11,13],firstproposedin[14],andthecloselyrelatedconceptsofActiveBlobs[21,22]andMorphableModels[6,18,24],arenon-linear,generative,andparametricmodelsofacertainvisualphenomenon.ThemostfrequentapplicationofAAMstodatehasbeenfacemodelling[19].However,AAMsmaybeusef
6、ulforotherphenomenatoo[18,22].Inatypicalapplication,thefirststepistofittheAAMtoaninputimage,i.e.modelparametersarefoundtomaximisethe“match”betweenthemodelinstanceandtheinputimage.Themodelparametersarethenusedinwhatevertheapplicationis.Forexample,theparameterscould
7、bepassedtoaclassifiertoyieldafacerecognitionalgorithm.Manydifferentclassificationtasksarepossible.In[19],forexample,thesamemodelwasusedforfacerecognition,poseestimation,andexpressionrecognition.FittinganAAMtoanimageisanon-linearoptimisationproblem.Theusualapproach
8、[7,10,11]istoiterativelysolveforincrementaladditiveupdatestotheparameters(theshapeandappearancecoefficients.)Giventhecurrentestimatesoftheshapeparameters,itispossiblet
