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1、AhierarchicalstatisticalframeworkforthesegmentationofdeformableobjectsinimagesequencesCharlesKervrannandFabriceHeitzIRISA/INRIA,CampusUniversitairedeBeaulieu,35042RennesCedex,FranceE-mail:kervrann@irisa.fr,heitz@irisa.frIEEEComputerVisionPatternRecognitionJune1994,Seattle,USAA
2、HierarchicalStatisticalFrameworkfortheSegmentationofDeformableObjectsinImageSequencesCharlesKervrannandFabriceHeitzIRISA/INRIA-RennesCampusUniversitairedeBeaulieuF-35042RennesCedex,FranceAbstractprocess;theycanbeseenasare nementoftheglobaldeformationsappliedtotheoriginalshape
3、.ThejointInthispaper,weproposeanewstatisticalframeworkfordistributionofthedeformabletemplateisderivedandmodelingandextracting2DmovingdeformableobjectsfromaMaximumAPosteriori(map)estimateofthede-imagesequences.Theobjectrepresentationreliesonahie-formationsisobtainedbyminimizing
4、aglobalenergyrarchicaldescriptionofthedeformationsappliedtoatem-(objective)functiondescribingtheinteractionsbet-plate.GlobaldeformationsaremodeledusingaKarhunenweenobservations(spatialortemporalgradientsex-Loeveexpansionofthedistorsionsobservedonarepre-tractedfromtheimage)andt
5、hedeformationprocess.sentativepopulation.LocaldeformationsaremodeledbyThemethodcombinestheadvantagesoffastglobala( rst-order)Markovprocess.Theoptimalbayesianes-optimizationtechniqueswithacompacthierarchicaltimateoftheglobalandlocaldeformationsisobtainedbystatisticaldescription
6、ofdeformations.Thisyieldsfastmaximizinganon-linearjointprobabilitydistributionusingmodeladjustmentandrobustsegmentation.stochasticanddeterministicoptimizationtechniques.TheComputervisionmethodsrelyingondeformableuseofglobaloptimizationtechniquesyieldsrobustandre-templatesareof
7、tenexpressedastheminimizationofliablesegmentationsinadversesituationssuchaslowsignal-(global)energyfunctionsdescribingtheinteractionsto-noiseratio,non-gaussiannoiseorocclusions.Moreo-betweentheobserveddataandthevariablesofthever,nohumaninteractionisrequiredtoinitializethemo-mo
8、del[4,8,9].Inmostmethods[1,5,7,8,9](apartdel.Theapproachisdem
