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1、HandGestureRecogrutionUsingHiddenMarkovModelsByung-WooMin,Ho-SubYoon,JungSoh,Yun-MoYangc'),andToskiakiEjimaUImageProcessingDiv./SystemsEngineeringResearchInstitute1Ueun-dong,Yuseong-gu,Taejeon,305-333Koreaemail:bwmin@cmke.seri.re.kr(')Dept.ofInformationEngineering,Kore
2、aUniversity,Chochiwon,Korea(")Dept.ofAI,KyushuInstituteofTechnology,Iizuka,JapanAbstractthatvocallanguage,gesturesandfacialexpressionsmustHandgesturerecognitionfromvisualimageshasabeintegratedforrealizingmorenaturalman-machinenuniberofpotentialapplicationinHCI(humancon
3、iputerinterfaceanddefinesitasamulti-modalinterface.interaction),machinevision,VR(virtua1reality),machineRecentlyresearchinhandgesturerecognitionaimsatcontrolintheindustryfield,andsoon.Mostconventionalapplyingtosignlanguagerecognition,controlofapproachestohandgesturerec
4、ognitionhaveemployedhouseholdelectronicappliances,human-computerdatagloves.But,formorenaturalinterface,handgestureinteraction,VR,andsoon.mustberecognizedfromvisualimagesasintheOurresearchisintendedtodevelopagraphiceditorcommunicationbetweenhumanswithoufusinganysystemop
5、eratedbyhandgestures.Thegesturesareexternaldevices.Ourresearchisintendedtodrawandeditclassifiedintotwotypes.Oneisthestaticgestureswhichgraphicelementsbyhandgesture.areone,two,three,four,fiveinKoreansignlanguageasUptonow,manymethodsforhandgestureshowninFigure1.recogniti
6、onhavebeenproposedsuchassyntacticalanalysis,neuralbasedapproach,HMM(hiddenMarkovmodel)basedrecognition.Asgestureisthecontinuousmotiononthesequentialtimeseries,HMMmustbeaprominentrecognitiontool.Thougheachanalysismethodhasme?ftsonetwothreefourfiveanddenierits,themostimp
7、ortantthinginhandgesturerecognitioniswhattheinputfeaturesarethatrepresentFigure1.5staticgesturesverywellthecharacteristicsofmovinghandgesture.Inourresearch,weconsidertheplanarhandgestureinfrontofTheotheristhedynamicgestureswhicharesixdrawingcameraandtherefore8-directio
8、nalchaincodesasinputelementssuchascircle,triangle,rectangle,arc,horizontalvectors.FortraininganHMMnetwork,asimplecont
