Domov Technika Rozpoznávání vzorů

Rozpoznávání vzorů



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.

Rozpoznávání statistických vzorů

Základním principem rozpoznávání statistických vzorů je:podobnévzorky jsou blízko sebe v prostoru jater a tvoří"skupinu",to je "shromáždění věcí". Metoda analýzy zařazuje daný vzor do tříd Cω1, k ω2, (Xictor=1xiding) 1,2,... ,N)měřenomodelem,...,ωc,apotomrozlišujteklasifikacipodlefunkcevzdálenostimezirežimy.Mezi nimi Představuje transpozici;Nispočetvzorkovýchbodů;rozlišujepočetvzorkovýchprvků.

Themainmethodsofstatisticalpatternrecognitionare:discriminantfunctionmethod,nearestneighborclassificationmethod,nonlinearmappingmethod,featureanalysismethod,principalfactoranalysismethod,etc.

Instatisticalpatternrecognition,Bayesiandecisionrulestheoreticallysolvetheproblemofoptimalclassifierdesign,butitsimplementationmustfirstsolvethemoredifficultproblemofprobabilitydensityestimation.BPneuralnetworklearnsdirectlyfromobservationdata(trainingsamples).Itisasimplerandmoreeffectivemethodandhasbeenwidelyused.However,itisaheuristictechnologyandlacksasolidtheoreticalbasisforspecifyingengineeringpractice.Thebreakthroughresultsoftheresearchofstatisticalinferencetheoryledtotheestablishmentofthemodernstatisticallearningtheory—VCtheory,whichnotonlysatisfactorilyansweredthetheoreticalquestionsthatappearedintheartificialneuralnetworkonastrictmathematicalbasis,butalsoderivedanewkindofThelearningmethod-SupportVectorMachine(SVM).

Oblasti použití

Rozpoznávání vzorů lze použít při rozpoznávání textu a řeči, dálkovém snímání a lékařské diagnostice.

①Rozpoznávání znaků

Chinesecharactershaveahistoryofthousandsofyears,andtheyarealsothemostfrequentlyusedcharactersintheworld.TheyhaveindeliblycontributedtotheformationanddevelopmentofthesplendidcultureoftheChinesenation.Therefore,withtheincreasingpopularityofinformationtechnologyandcomputertechnology,howtoinputtextintocomputersconvenientlyandquicklyhasbecomeanimportantbottleneckthataffectstheefficiencyofhuman-computerinterfaces,anditisalsorelatedtowhethercomputerscantrulybepopularizedinourcountry.Chinesecharacterinputismainlydividedintotwotypes:manualkeyboardinputandautomaticmachinerecognitioninput.Amongthem,manualtypingisslowandlabor-intensive;automaticinputisdividedintoChinesecharacterrecognitioninputandvoicerecognitioninput.Intermsofthedifficultyofrecognitiontechnology,thedifficultyofhandwritingrecognitionishigherthanthatofprintrecognition,andinhandwritingrecognition,thedifficultyofofflinehandwritingfarexceedsthatofonlinehandwritingrecognition.Inadditiontothepracticalapplicationofofflinehandwrittendigitrecognition,offlinehandwrittenrecognitionofChinesecharactersandothercharactersisstillinthelaboratorystage.

②Rozpoznávání řeči

Thefieldsofspeechrecognitiontechnologyinclude:signalprocessing,patternrecognition,probabilitytheoryandinformationtheory,soundmechanismandhearingmechanism,artificialintelligenceandsoon.Inrecentyears,inthefieldofbiometrictechnology,voiceprintrecognitiontechnologyhasattractedworldwideattentionduetoitsuniqueadvantagessuchasconvenience,economy,andaccuracy,andhasincreasinglybecomeanimportantandpopularsecurityverificationmethodinpeople'sdailylifeandwork.Moreover,thespeechrecognitionmethodthatusesgeneticalgorithmstotrainthecontinuoushiddenMarkovmodelhasbecomethemainstreamtechnologyofspeechrecognition.Thismethodhasafasterrecognitionspeedduringspeechrecognitionandahigherrecognitionrate.

③Rozpoznávání otisků prstů

Theunevenskinontheinnersurfaceofourpalms,fingers,feet,andtoeswillformavarietyofpatterns.Thepatterns,breakpointsandintersectionsoftheseskinsaredifferentandunique.Relyingonthisuniqueness,apersoncanbematchedwithhisfingerprints,andhistrueidentitycanbeverifiedbycomparinghisfingerprintswithpre-savedfingerprints.Generally,fingerprintsaredividedintothefollowingmajorcategories:loop,whorl,andarch.Inthisway,fingerprintsofeachpersoncanbeclassifiedandretrievedseparately.Fingerprintrecognitioncanbasicallybedividedintoseveralmajorsteps:preprocessing,featureselectionandpatternclassification.

④Dálkové snímání

Remotesensingimagerecognitionhasbeenwidelyusedincropyieldestimation,resourceprospecting,weatherforecastingandmilitaryreconnaissance.

⑤Lékařská diagnostika

Patternrecognitionhasachievedresultsincancercelldetection,X-rayphotoanalysis,bloodtest,chromosomeanalysis,electrocardiogramdiagnosisandelectroencephalogramdiagnosis.

Developmentpotential

Patternrecognitiontechnologyisthebasictechnologyofartificialintelligence.The21stcenturyisacenturyofintelligence,informationization,computing,andnetworking.Thisischaracterizedbydigitalcomputing.Inthenextcentury,patternrecognitiontechnology,asabasicsubjectofartificialintelligencetechnology,willsurelygainhugeroomfordevelopment.Internationally,majorauthoritativeresearchinstitutionsandmajorcompanieshavebeguntoattachimportancetopatternrecognitiontechnologyasthecompany'sstrategicresearchanddevelopmentfocus.

1,Technologie rozpoznávání hlasu

VoicerecognitiontechnologyisgraduallybecomingtheHumanComputerInterface(HumanComputerInterface,ThekeytechnologyofHCI),theapplicationofvoicetechnologyhasbecomeacompetitiveemerginghigh-techindustry.ThemarketforecastoftheChinaInternetCenter:Inthenext5years,theChinesevoicetechnologyfieldwillhaveamarketcapacityofmorethan40billionyuan,andthenitwillgrowatarateofmorethan30%everyyear.

2.Biometricsauthenticationtechnology

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.

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