Developmenthistory
Earlypatternrecognitionresearchfocusedonmathematicalmethods.Inthelate1950s,F.Rosenblattproposedasimplifiedmathematicalmodelthatsimulatestherecognitionofthehumanbrain-theperceptron,whichinitiallyrealizesthetrainingoftherecognitionsystemthrougheachsampleofagivencategory,sothatthesystemhastheabilitytorecognizeotherunknownsafterlearning.Theabilitytoclassifythemodelcorrectly.In1957,ZhouShaokangproposedtheuseofstatisticaldecision-makingtheorytosolvepatternrecognitionproblems,whichpromotedtherapiddevelopmentofpatternrecognitionresearchthatbeganinthelate1950s.In1962,R.Narassimanproposedasyntacticrecognitionmethodbasedonprimitiverelations.FuJingsun(K.S.Fu)conductedsystematicandfruitfulresearchonthetheoryandapplicationofRuo,andpublishedamonograph"SyntaxPatternRecognitionandItsApplication"in1974.In1982and1984,J.Hofffieldpublishedtwoimportantpapers,whichdeeplyrevealedtheassociativestorageandcomputingcapabilitiesofartificialneuronsandnetworks,andfurtherpromotedtheresearchworkofpatternrecognition.Injustafewyears,significantresultshavebeenachievedinmanyapplications.,Thusforminganewsubjectdirectionoftheartificialneuralnetworkmethodofpatternrecognition.
Whenpeopleobservethingsorphenomena,theyoftenlookforthedifferencesbetweenthemandotherthingsorphenomena,andgroupallsimilarbutnotidenticalthingsorphenomenaintooneaccordingtoacertainpurpose.kind.Characterrecognitionisatypicalexample.Forexample,thenumber"4"canbewritteninvariousways,buttheyallbelongtothesamecategory.Moreimportantly,evenifacertainwayofwriting"4"hasnotbeenseenbefore,itcanbeclassifiedintothecategorytowhich"4"belongs.Thiskindofthinkingabilityofthehumanbrainconstitutestheconceptof"mode".Intheaboveexample,theconceptofpatternandsetareseparated.Aslongasyouknowalimitednumberofthingsorphenomenainthisset,youcanidentifyanynumberofthingsorphenomenathatbelongtothisset.Inordertoemphasizeinferringthetotalityofthingsorphenomenafromsomeindividualthingsorphenomena,wecallsuchindividualthingsorphenomenavariouspatterns.Somescholarsalsobelievethattheentirecategoryshouldbecalledamodel.Sucha"model"isanabstractconcept.Forexample,"houses"areall"models",andspecificobjects,suchastheGreatHallofthePeople,arecalledmodels.Asampleofthe"house"typeofmodel.Thedifferentmeaningsofsuchnounsareeasytoclarifyfromthecontext.
Patternrecognitionisabasichumanintelligence.Indailylife,peopleoftenperform"patternrecognition".Withtheadventofcomputersinthe1940sandtheriseofartificialintelligenceinthe1950s,peoplecertainlyhopetousecomputerstoreplaceorexpandpartofhumanmentalwork.(Computer)patternrecognitiondevelopedrapidlyandbecameanewsubjectintheearly1960s.
Patternrecognitionreferstotheprocessingandanalysisofvariousformsofinformation(numerical,literal,andlogicalrelations)thatcharacterizethingsorphenomena,inordertodescribe,identify,classifyandanalyzethingsorphenomena.Theprocessofexplanationisanimportantpartofinformationscienceandartificialintelligence.
Researchdirection
Patternrecognitionisalsooftencalledpatternclassification.Fromtheperspectiveofthenatureoftheproblemandthemethodofsolvingtheproblem,thepatternrecognitionisdividedintosupervisedclassification(SupervisedClassification)Andunsupervisedclassification(UnsupervisedClassification)twokinds.Themaindifferencebetweenthetwoiswhetherthecategorytowhicheachexperimentalsamplebelongsisknowninadvance.Generallyspeaking,supervisedclassificationoftenneedstoprovidealargenumberofsamplesofknowncategories,butinpracticalproblems,therearecertaindifficulties,soitbecomesnecessarytostudyunsupervisedclassification.
Thepatterncanalsobedividedintotwoforms:abstractandconcrete.Theformer,suchasconsciousness,thought,discussion,etc.,belongtothecategoryofconceptrecognitionresearch,whichisanotherresearchbranchofartificialintelligence.Thepatternrecognitionwearereferringtomainlyreferstotheidentificationandclassificationofspecificpatternsofobjectssuchasspeechwaveforms,seismicwaves,electrocardiograms,electroencephalograms,pictures,photos,texts,symbols,andbiosensors.
Patternrecognitionresearchmainlyfocusesontwoaspects.Oneishowgraduatestudentsperceiveobjects(includingpeople),whichbelongstothecategoryofcognitivescience,andtheotherishowtousecomputerstoimplementpatternsunderagiventask.Theoriesandmethodsofidentification.Theformeristheresearchcontentofphysiologists,psychologists,biologistsandneurophysiologists.Thelatterhasachievedsystematicresearchresultsthroughtheeffortsofmathematicians,informaticsexpertsandcomputerscientistsinrecentdecades.
Useacomputertoidentifyandclassifyagroupofeventsorprocesses.Theidentifiedeventsorprocessescanbespecificobjectssuchastext,sound,andimages,orabstractobjectssuchasstateanddegree.Theseobjectsaredistinguishedfrominformationindigitalformandarecalledmodeinformation.
Thenumberofcategoriesclassifiedbypatternrecognitionisdeterminedbythespecificrecognitionproblem.Sometimes,theactualnumberofcategoriescannotbeknownatthebeginning,andtherecognitionsystemneedstorepeatedlyobservetherecognizedobjectandthendetermineit.
Patternrecognitionisrelatedtostatistics,psychology,linguistics,computerscience,biology,cybernetics,etc.Ithasacrossrelationshipwiththeresearchofartificialintelligenceandimageprocessing.Forexample,theadaptiveorself-organizingpatternrecognitionsystemincludesthelearningmechanismofartificialintelligence;thesceneunderstandingandnaturallanguageunderstandingofartificialintelligenceresearchalsoincludepatternrecognitionproblems.Anotherexampleistheapplicationofimageprocessingtechnologyinthepreprocessingandfeatureextractionofpatternrecognition;theimageanalysisinimageprocessingalsoappliesthetechnologyofpatternrecognition.
Researchmethod
Decisiontheorymethod
Alsoknownasstatisticalmethod,itisanearlierandmorematuremethod.Theidentifiedobjectisfirstdigitizedandtransformedintodigitalinformationsuitableforcomputerprocessing.Apatternisoftenrepresentedbyalargeamountofinformation.Manypatternrecognitionsystemsalsoperformpre-processingafterthedigitizationprocesstoremovethemixedinterferenceinformationandreducesomedistortionsanddistortions.Followedbyfeatureextraction,thatis,extractasetoffeaturesfromthedigitizedorpreprocessedinputpattern.Theso-calledfeatureisaselectedmeasure,whichremainsunchangedoralmostunchangedforgeneraldeformationanddistortion,andcontainsonlyaslittleredundantinformationaspossible.Thefeatureextractionprocessmapstheinputpatternfromtheobjectspacetothefeaturespace.Atthistime,thepatterncanberepresentedbyapointorafeaturevectorinthefeaturespace.Thiskindofmappingnotonlycompressestheamountofinformation,butisalsoeasytoclassify.Indecision-makingtheorymethods,featureextractionoccupiesanimportantposition,butthereisnogeneraltheoreticalguidance.Onlybyanalyzingspecificidentificationobjectstodeterminewhichfeaturetoselect.Afterfeatureextraction,itcanbeclassified,thatis,remapfromthefeaturespacetothedecisionspace.Forthisreason,adiscriminantfunctionisintroduced,thediscriminantfunctionvaluecorrespondingtoeachcategoryiscalculatedfromthefeaturevector,andtheclassificationisperformedbycomparingthediscriminantfunctionvalue.
Syntacticmethod
Alsoknownasstructuralmethodorlinguisticmethod.Thebasicideaistodescribeapatternasacombinationofsimplersub-patterns,whichcanbedescribedasacombinationofsimplersub-patterns,andfinallygetatree-likestructuredescription.Thesimplestsub-patternatthebottomiscalledPatternprimitives.Theproblemofselectingprimitivesinthesyntacticmethodisequivalenttotheproblemofselectingfeaturesinthedecisiontheorymethod.Itisusuallyrequiredthattheselectedprimitivescanprovideacompactdescriptionofthepatternthatreflectsitsstructuralrelationship,andshouldbeeasytoextractbynon-syntacticmethods.Obviously,theprimitiveitselfshouldnotcontainimportantstructuralinformation.Apatternisdescribedbyasetofprimitivesandtheircombination,calledapatterndescriptionsentence,whichisequivalenttocombiningsentencesandphraseswithwords,andwordswithcharacters.Therulesforcombiningprimitivesintopatternsarespecifiedbyso-calledgrammars.Oncetheprimitivesareidentified,therecognitionprocesscanbecarriedoutthroughsyntacticanalysis,thatis,whetherthegivenpatternsentenceconformstothespecifiedgrammar,andthosethatsatisfyacertaintypeofgrammarareclassifiedintothatcategory.
Thechoiceofpatternrecognitionmethoddependsonthenatureoftheproblem.Iftheidentifiedobjectisextremelycomplexandcontainsrichstructuralinformation,thesyntacticmethodisgenerallyadopted;theidentifiedobjectisnotverycomplexordoesnotcontainobviousstructuralinformation,andthedecision-makingtheorymethodisgenerallyadopted.Thesetwomethodscannotbecompletelyseparated.Inthesyntacticmethod,theprimitivesthemselvesareextractedbythemethodofdecisiontheory.Inapplication,combiningthesetwomethodsandapplyingthematdifferentlevelscanoftenachievebetterresults.
Tilastokuvioiden tunnistaminen
Tilastollisen kuvion tunnistamisen perusperiaate on:samanlaiset näytteet ovat lähellä toisiaan kuviotilassa ja muodostavat "ryhmän", eli "Thingsthertogether". ,...,xid)T(i=1,2,... ,N)mallilla mitattuna,...,ωc,ja erottelee luokituksen tilojen välisen etäisyysfunktion mukaan.Niistä edustaa transpositiota;Nisnäytepisteiden lukumäärä;erottaa näyteominaisuuksien lukumäärän.
Themainmethodsofstatisticalpatternrecognitionare:discriminantfunctionmethod,nearestneighborclassificationmethod,nonlinearmappingmethod,featureanalysismethod,principalfactoranalysismethod,etc.
Instatisticalpatternrecognition,Bayesiandecisionrulestheoreticallysolvetheproblemofoptimalclassifierdesign,butitsimplementationmustfirstsolvethemoredifficultproblemofprobabilitydensityestimation.BPneuralnetworklearnsdirectlyfromobservationdata(trainingsamples).Itisasimplerandmoreeffectivemethodandhasbeenwidelyused.However,itisaheuristictechnologyandlacksasolidtheoreticalbasisforspecifyingengineeringpractice.Thebreakthroughresultsoftheresearchofstatisticalinferencetheoryledtotheestablishmentofthemodernstatisticallearningtheory—VCtheory,whichnotonlysatisfactorilyansweredthetheoreticalquestionsthatappearedintheartificialneuralnetworkonastrictmathematicalbasis,butalsoderivedanewkindofThelearningmethod-SupportVectorMachine(SVM).
Sovellusalueet
Kuviontunnistusta voidaan käyttää tekstissä ja puheentunnistuksessa, etätunnistuksessa ja lääketieteellisessä diagnoosissa.
①Hahmon tunnistus
Chinesecharactershaveahistoryofthousandsofyears,andtheyarealsothemostfrequentlyusedcharactersintheworld.TheyhaveindeliblycontributedtotheformationanddevelopmentofthesplendidcultureoftheChinesenation.Therefore,withtheincreasingpopularityofinformationtechnologyandcomputertechnology,howtoinputtextintocomputersconvenientlyandquicklyhasbecomeanimportantbottleneckthataffectstheefficiencyofhuman-computerinterfaces,anditisalsorelatedtowhethercomputerscantrulybepopularizedinourcountry.Chinesecharacterinputismainlydividedintotwotypes:manualkeyboardinputandautomaticmachinerecognitioninput.Amongthem,manualtypingisslowandlabor-intensive;automaticinputisdividedintoChinesecharacterrecognitioninputandvoicerecognitioninput.Intermsofthedifficultyofrecognitiontechnology,thedifficultyofhandwritingrecognitionishigherthanthatofprintrecognition,andinhandwritingrecognition,thedifficultyofofflinehandwritingfarexceedsthatofonlinehandwritingrecognition.Inadditiontothepracticalapplicationofofflinehandwrittendigitrecognition,offlinehandwrittenrecognitionofChinesecharactersandothercharactersisstillinthelaboratorystage.
②Puheentunnistus
Thefieldsofspeechrecognitiontechnologyinclude:signalprocessing,patternrecognition,probabilitytheoryandinformationtheory,soundmechanismandhearingmechanism,artificialintelligenceandsoon.Inrecentyears,inthefieldofbiometrictechnology,voiceprintrecognitiontechnologyhasattractedworldwideattentionduetoitsuniqueadvantagessuchasconvenience,economy,andaccuracy,andhasincreasinglybecomeanimportantandpopularsecurityverificationmethodinpeople'sdailylifeandwork.Moreover,thespeechrecognitionmethodthatusesgeneticalgorithmstotrainthecontinuoushiddenMarkovmodelhasbecomethemainstreamtechnologyofspeechrecognition.Thismethodhasafasterrecognitionspeedduringspeechrecognitionandahigherrecognitionrate.
③Sormenjälkien tunnistus
Theunevenskinontheinnersurfaceofourpalms,fingers,feet,andtoeswillformavarietyofpatterns.Thepatterns,breakpointsandintersectionsoftheseskinsaredifferentandunique.Relyingonthisuniqueness,apersoncanbematchedwithhisfingerprints,andhistrueidentitycanbeverifiedbycomparinghisfingerprintswithpre-savedfingerprints.Generally,fingerprintsaredividedintothefollowingmajorcategories:loop,whorl,andarch.Inthisway,fingerprintsofeachpersoncanbeclassifiedandretrievedseparately.Fingerprintrecognitioncanbasicallybedividedintoseveralmajorsteps:preprocessing,featureselectionandpatternclassification.
④Etähaku
Remotesensingimagerecognitionhasbeenwidelyusedincropyieldestimation,resourceprospecting,weatherforecastingandmilitaryreconnaissance.
⑤Lääketieteellinen diagnoosi
Patternrecognitionhasachievedresultsincancercelldetection,X-rayphotoanalysis,bloodtest,chromosomeanalysis,electrocardiogramdiagnosisandelectroencephalogramdiagnosis.
Developmentpotential
Patternrecognitiontechnologyisthebasictechnologyofartificialintelligence.The21stcenturyisacenturyofintelligence,informationization,computing,andnetworking.Thisischaracterizedbydigitalcomputing.Inthenextcentury,patternrecognitiontechnology,asabasicsubjectofartificialintelligencetechnology,willsurelygainhugeroomfordevelopment.Internationally,majorauthoritativeresearchinstitutionsandmajorcompanieshavebeguntoattachimportancetopatternrecognitiontechnologyasthecompany'sstrategicresearchanddevelopmentfocus.
1,äänentunnistustekniikka
VoicerecognitiontechnologyisgraduallybecomingtheHumanComputerInterface(HumanComputerInterface,ThekeytechnologyofHCI),theapplicationofvoicetechnologyhasbecomeacompetitiveemerginghigh-techindustry.ThemarketforecastoftheChinaInternetCenter:Inthenext5years,theChinesevoicetechnologyfieldwillhaveamarketcapacityofmorethan40billionyuan,andthenitwillgrowatarateofmorethan30%everyyear.
2.Biometriset tunnistustekniikka
Biometricsisthemostconcernedsecurityauthenticationtechnologyofthiscentury.Itsdevelopmentisthegeneraltrend.Peoplearewillingtoforgetallpasswords,throwawayallmagneticcards,andusetheiruniquenesstoidentifyandkeepsecret.TheInternationalDataGroup(IDG)predictsthatbiometrics,thebasiccoretechnologyofmobilee-commerce,astheinevitabledevelopmentdirectioninthefuture,willreachamarketsizeof10billionUSdollarsinthenext10years.
3,DigitalWatermarkingTechnology
DigitalWatermarkingTechnology(DigitalWatermarkingTechnology)thathasonlybeguntodevelopinternationallysincethe1990s)Isthemostpromisingandadvantageousdigitalmediacopyrightprotectiontechnology.IDCpredictsthattheglobalmarketcapacityofdigitalwatermarkingtechnologywillexceedUS$8billioninthenextfiveyears.
Fromthedevelopmentofpatternrecognitioninthe1920stothepresent,people’scommonbeliefisthatthereisnosinglemodelandsingletechnologyforsolvingallpatternrecognitionproblems.Allwehaveisatool.Whatneedstobedoneistocombinespecificproblemswithstatisticalandsyntacticrecognition,combinestatisticalpatternrecognitionorsyntacticpatternrecognitionwithheuristicsearchinartificialintelligence,andcombinestatisticalpatternrecognitionorsyntacticpatternrecognitionwithsupportvectormachines.Combiningmachinelearning,combiningartificialneuronnetworkswithvariousexistingtechnologies,expertsystemsinartificialintelligence,anduncertainreasoningmethods,in-depthunderstandingoftheeffectivenessandpotentialofvarioustools,learningfromeachother’sstrengths,andcreatingAnewaspectofpatternrecognitionapplications.
Therearevarioustheoreticalexplanationsfortheabilitytorecognizetwo-dimensionalpatterns.Thetemplatetheorybelievesthateverypatternweknowhasacorrespondingtemplateorminiaturecopyinlong-termmemory.Patternrecognitionistomatchthemostsuitabletemplateforvisualstimuli.Thefeaturetheorybelievesthatvisualstimuliarecomposedofvariouscharacteristics,andpatternrecognitionistocomparethecharacteristicsofthestimuluswiththepatterncharacteristicsstoredinlong-termmemory.Featuretheoryexplainssomebottom-upprocessesinpatternrecognition,butitdoesnotemphasizeenvironment-basedinformationandexpectedtop-downprocessing.Thetheorybasedonstructuredescriptionmaybemoreappropriatethanthetemplatetheoryorfeaturetheory.