文献翻译——车牌识别（LPR）：一个马来西亚案例研究实验的复习

外文文献资料TheInternationalJournalofSoftComputingandSoftwareEngineeringJSCSE,Vol.3,No.3,SpecialIssue:TheProceedingofInternationalConferenceonSoftComputingandSoftwareEngineering2013SCSE13,SanFranciscoStateUniversity,CA,U.S.A.,March2013Doi:10.7321/jscse.v3.n3.15e-ISSN:2251-754583LicensePlateRecognition(LPR):AReviewwithExperimentsforMalaysiaCaseStudyNuzulhaKhilwaniIbrahim,EmalianaKasmuri,NoraziraAJalil,MohdAdiliNorasikin,SazilahSalamFacultyofInformationandCommunicationTechnology,UniversitiTeknikalMalaysiaMelaka(UTeM),HangTuahJaya,76100Melaka,Malaysianuzulha|emaliana|adili|.myMohamadRiduwanMdNawawiFacultyofElectricalEngineering,UniversitiTeknikalMalaysiaMelaka(UTeM),HangTuahJaya,76100Melaka,M.myAbstractMostvehiclelicenseplaterecognitionuseneuralnetworktechniquestoenhanceitscomputingcapability.Theimageofthevehiclelicenseplateiscapturedandprocessedtoproduceatextualoutputforfurtherprocessing.Thispaperreviewsimageprocessingandneuralnetworktechniquesappliedatdifferentstageswhicharepreprocessing,filtering,featureextraction,segmentationandrecognitioninsuchwaytoremovethenoiseoftheimage,toenhancetheimagequalityandtoexpeditethecomputingprocessbyconvertingthecharactersintheimageintorespectivetext.AnexemplarexperimenthasbeendoneinMATLABtoshowthebasicprocessoftheimageprocessingespeciallyforlicenseplateinMalaysiacasestudy.Analgorithmisadaptedintothesolutionforparkingmanagementsystem.Thesolutionthenisimplementedasproofofconcepttothealgorithm.Keyword-imageprocessing,preprocessing,filtering,featureextraction,segmentation,recognition,experimentI.INTRODUCTIONTheadvancedofcomputerapplicationprocessedmorethantextualdatasolvingeverydayproblems.Inputsfromopticaldeviceareusedindomainapplicationsuchasmedical,security,monitoringandcontrolandengineering.Abilityforcomputertoprocessimageandtranslateitintosomethingmeaningfulhasbecomemorepopular.Therefore,thetechnologyofimageprocessinghasadoptedinmanagingvehicleparkingsystem,vehicleaccesstorestrictedarea,trafficmonitoringsystemandhighwayelectronictollcollection.Forthispurpose,thecomputerneedstocapturethevehiclelicenceplatenumberandprocessitinthecomputer.Acameracapturestheimageofvehiclelicenseplate.Theimagethenfeedintothecomputerforfurtherprocessing.Theoutputoffromtheprocessisthevehiclelicenseplatenumberintextualform.Foraparkingsystem,theoutputisusedforcaridentification,parkingpaymentandauthorizationtoaccessintotheparkingspace.Thispaperreviewstheprocessingofvehiclelicenseplatethatusesimageprocessingandneuralnetworktechnique.Theframeworkforthisresearchisadaptedfrompreviousstudies1-4asshowninFigure1whichincludes5stages:pre-processing,(b)filtering,(c)featureextraction,(d)segmentationand(e)characterrecognition.Thefinaloutputofthesampleexperimentistorecognizethealphanumericcharactersonthelicenseplate.Thestructureofthispaperisorganizedbythestagesoftheprocess.II.PREPROCESSINGDigitalimagepreprocessingisaninitialsteptoimageprocessingimprovingthedataimagequalityformoresuitableforvisualperceptionorcomputationalprocessing.Preprocessingremoveunwanteddataandenhancetheimagebyremovingbackgroundnoise,normalizingtheintensityofindividualimageparticles,imagedeblurandremoveimagereflections.Preprocessingforcarlicenseplatenumberusesthreecommonsubprocesses,whicharegeometricoperation,grayscalingprocessandbinarizationprocess.Manyneuralnetworktechniqueshavebeenappliedtothesepreprocessingtechniquesmainlytoproducebetterimageandtoincreasethespeedofconvergenceofanimage.A.GeometricOperationGeometricoperationisaprocesstolocatethecarlicenseplate.Thepurposeofthisoperationistolocalizethecarplateforfastercharacteridentificationoverasmallregion.AnimprovedBackPropagationnetworkisusedtoovercometheweaknessofconvergencespeedin1.Geneticalgorithmandmomentumtermisintroducedtothecurrentnetworktoincreasethespeedofconvergencerate.ThecurrentBPnetworklearningprocessissaidtobeeasilyproduceerrorifinitialweightsisnotsetproperly1anditisdifficulttodeterminethenumberofhiddenlayerandhiddennodes.TheimprovednetworkusingBPmomentumincreasethespeedandtheaccuracytolocalizethecarlicenseplacelocation.Agrayscaleimageextractstheedgeofthelicenseplateusingsobeloperator1.MalviyaandBhirudin2usesiterativethresholdingoperationtoidentifylicenseplateofavehicle.Objectswithgeometriccharacteristicsarelabelledandselected.Theprocesstakesintoaccountaspectratio,totalpixelperobject,height,widthandthepresenceofcharactersintheregion.Forthis,weproposethefollowingalgorithm,wherethepseudo-codecanbesimplifiedasthefollowing:togetthescaleoftheimageforx-axisandy-axistoassignthenewvalueofhorizontalandverticalaxisbasedonthescaleofthex-axisandy-axistogetthegrayscalethresholdingvalueoftheimageTheinputoftheexperimentisshownasFigure2whiletheexampleoutputcanbeviewedasFigure3.Figure2.InputdataforLicensePlateImageProcessingFigure3.ImageoutputaftergeometricoperationprocessTheoutputfromtheextractionprocesswillbeusedinthenextstagewhichisgrayscalingprocess.B.GrayscalingProcessGrayscalingisaprocesstoproduceagrayscaleimagefromamulticolorimage.Inthisprocess,thethresholdofanimageiscalculated.Ifitislessthanthethreshold,theimagedataisrecalculatedtogetthecorrectgrayscalevalue.Thepurposeofthresholdingistoseparatetheobjectofinterestfromthebackground.Thresholdingisimportanttoprovidesufficientcontrastfortheimagesothatdifferentlevelofintensitybetweenobjectandthebackgroundcanbedifferentiatedforlatercomputationalprocessing.Differentintensitydeterminesthevalueofthethreshold.Grayscalingprocessimprovesthequalityoftheimageforlatercomputationalprocessing.Otherpreprocessingtechniquestoimprovethequalityoftheimageincludingimagedeblurring,imageenhancement,imagefusionandimagereconstruction.Imagefusionisaprocesstoenhancetheimagewithmultiplecombinationsofimages2-3.Thisprocessissuitabletoidentifythecarlicenseregistrationnumberfromamovingcar.Thetechniqueintegratesmultiresolutionimageandproduceacompositeimageusinginversemultiresolutiontransform3.Atemplateofimagefromagrayscaleisshiftedtoverticalandhorizontaldirection.Thecontrastfrequencyiscalculatedforeachpositioninthetemplateandcreatesanewimageusingthresholdingprocedure.Anycolorbelowthethresholdissettoback(zero)andabovethresholdissettowhite(one).Thevaluedeterminesthegraylevelresultingblackandwhiteimage.Atrainedfeedforwardneuralnetwork(FFN)withBlockRecursiveLSalgorithmisusedtoprocesscarlicenseplate4.Theapproachistoimprovetheconvergencerateandstabilizetherobustnessofthesolution.ThelocationofthecarlicenseplateisextractedusingDiscreteFourierTransform(DFT).DFTidentifiesmaximumvalueofhorizontalandverticaledges.Priortothattoneequalizationandcontrastreductionisusedtoimprovetheimage.Thesetechniquesarepreferredbecauseitismorerobustandsuitablecomparedtoedgeenhancement.Forthis,weproposethefollowingalgorithm,wherethepseudo-codecanbesimplifiedasthefollowing:toconvertintograyscaleimageThepseudo-codecanbetranslatedinMATLABsuchasfollowing:TestImg1=rgb2gray(TestImg1);C.BinarizationProcessBinarizationisaprocessofconvertinggrayscaleimageintoblackandwhiteimageor“0”and“1”.Previously,thegrayscaleimageconsistsofdifferentlevelofgrayvalues;from0to255.Toimprovethequalityandextractsomeinformationfromtheimage,theimageneedstobeprocessafewtimesandthusmakethebinaryimagemoreuseful.Graythresholdvalueofanimageisrequiredinthebinarizationprocessasitisimportanttodeterminewhetherthepixelsthathavinggrayvalueswillbeconvertedtoblackorwhite.Forthis,weproposethefollowingalgorithm,wherethepseudo-codecanbesimplifiedasthefollowing:toconvertintoblackandwhiteimageThepseudo-codecanbetranslatedinMATLABsuchasfollowing:ImgBW=im2bw(TestImg1,thresholdTheexampleoutputcanbeviewedasFigure4.Figure4.ImageoutputafterbinarizationprocessTheoutputfromtheextractionprocesswillbeusedinthenextstageoftheprocessinginthisframeworkwhichisfiltering.III.FILTERINGToenhancethequalityofprocessingimage,filteringisrequiredtosolvecontrastenhancement,noisesuppression,blurryissueanddatareduction.ItisreportedthatmostofpreprocessingactivitiesconductedinimagerestorationapplyNeuralNetworkapproach5.Rectanglesfilteringimplementedontherealplatenumberinvolvesconvolutionmatrix,binarizationfilterwithverticalandhorizontalprojectionabletoenhancetheimagequalityandeliminatesunwantedpiecesontheplate.Itisalsorecognizethenumberofrowsandsymbolsintheplatenumber6.In7,asimplefilterisdesignedbyimplementingintensityvarianceandedgedensitytoovercomeilluminationissue,distancechangedandcomplexbackground.Itisproposedthatthisapproachconvenientforreal-timeapplication.Thequalityandselectionofparametersonthecameraextremelycontributesthedesiredpreprocessingimagequality8.TheexampleoutputcanbeviewedasFigure5.Theoutputfromtheextractionprocesswillbeusedinthenextstagewhichisimagesegmentation.Figure5.ImageoutputafterfilteringprocessIV.FEATUREEXTRACTIONFeaturesextractionisthepartofmeasuringthoserelevantfeaturestobeusedinrecognitionprocess.Selectionoftherightfeaturesisimportantinordertoobtainbestresultsinlicenseplaterecognitionstudy.Colourfeaturesareverygoodpotentialforobjectdetection.Howevertheparametersuchascolourofcar,illuminationconditionandthequalityofimagingsystemhasbeenlimiteditspractice9.Accordingto10,colourfeatureshavebeenstudiedby11and12butfromthestudy,thisfeaturenotrobustenoughtovariousenvironments.However,therearemanytypesoffeaturesthatcanaidlicenseplaterecognitionsuchasaspectratio,texture,edgedensity,andsizeofregion10.Inordertoachievebetterdetectionrateinlicenseplaterecognition,researchersin10and13hadsuggestedacombinationoffeatures.Forinstance,apromisingresultforcombinationofcolourandedgehasbeenreportedin14.Moreover,9hasreportedthattheuseofsimplegeometricalfeaturessuchasshape,aspectratio,andsizeareenoughtofindgenuinelicenseplate.Howevertheresearchersfaceproblemsuchasclutterpartsintheimageandovercomeitwithedgedensity.Edgefeaturesofthecarimageareveryimportant,andedgedensitycanbeusedtosuccessfullydetectanumberplatelocationduetothecharacteristicsofthenumberplate9.Theedgedensityfeatureshadbeenusedin9,10,13becausethedensityofverticaledgesatthelicenseplateareaisconsiderablyhigherthanitsneighbourhood.Inaddition,thisfeatureismorereliableandabletoreduceprocessingtime.Littlecomputationaltimeisoneofimportantelementinrecognitionespeciallyinreal-timedetection.However,thereisalwaystrade-offbetweenthenumberoffeaturesusedinthesystemandthecomputationaltime9,13.Forthis,weproposethefollowingalgorithm,wherethepseudo-codecanbesimplifiedasthefollowing:Tocomparetheverticalandhorizontalhistogramingettingtherequiredfeatures.toextractthemeaningfulimagebasedonthefeaturesselectedThen,thehorizontalandverticalhistogramsarecombinedtogetthematchingregionofalicenseplateiskeptascandidateregionoralsoknownasmeaningfulimage.TheexampleoutputcanbeviewedasFigure6andFigureFigure6.ImageoutputafterfeatureselectionprocessFigure7.ImageoutputafterfeatureextractionprocessTheoutputfromtheextractionprocesswillbeusedinthenextstagewhichisimagesegmentation.V.IMAGESEGMENTATIONOneofthemostpopulartopicsinimageprocessingstudyisimagesegmentation.Thesegmentationprocessbecomesimportanttotheprocessingoftheimagetofindthemeaningfulinformationwhereitcomesfromthemeaningfulregionswhichrepresenthigherlevelofdata.Theanalysisofimagerequireslargeamountoflowlevelofdatawhichisinpixeltobeextractedintomeaningfulinformation.Higher-levelobjectpropertiescanbeincorporatedintosegmentationprocess,aftercompletingcertainpreliminarysegmentationprocess.Examplesofhigher-levelpropertiesareasfollow:i.shape,orii.colourfeaturesThen,itcomestothegoalofsegmentationwhichistofindregionsthatrepresentmeaningfulpartsofobjects.Insegmentation,theimagewillbedividedintoregionsbasedontheinterestofthestudy.Imagesegmentationmethodswilllookforobjectsthateitherhavesomemeasureofhomogeneity(withinthemselves),orcontrast(withtheobjectsontheirborder).Mostimagesegmentationalgorithmscanbedividedasthefollowing:i.modifications,ii.extensions,binationofthese2basicconceptsClassically,Umbaughin15divideimagesegmentationtechniquesintothree(3)whichare:i.Regiongrowingandshrinking:subsetofclusteringii.Clusteringmethodsiii.Boundarydetection:extensionsoftheedgedetectiontechniquesAtthesamepoint,HaralickandShapiro16categorizedimagesegmentationtechniquesintosix(6)whichare:i.Measurementspaceguidedspatialclusteringii.Singlelinkageregiongrowingschemesiii.Hybridlinkageregiongrowingschemesiv.Centroidlinkageregiongrowingschemesv.Spatialclusteringschemesvi.SplitandmergeschemesClusteringisoneofthesegmentationtechniqueasHaralickandShapiro16differentiatedclusteringandsegmentationsuchasfollow:i.Inclustering:thegroupingisdoneinmeasurementspaceii.Insegmentation:thegroupingisdoneinthespatialdomainoftheimageClusteringtechniquescanbeusedtoanydomain,eg:anyN-dimensionalcolororfeaturespace,includingspatialdomainscoordinates.Thistechniquesegmentstheimagebyplacingsimilarelementsintogroups,orclusters,basedonsomesimilaritymeasure.Clusteringisdifferfromregiongrowingandshrinkingmethods,wherethemathematicalspaceusedforclustering.Thedetailsofeachmethodsinsegmentationareexplainedinthenextsections.A.ThresholdingThresholdingisoneofthesimplestandmostpopularmethodinimagesegmentation.Twocommontypesofthresholdingareoutlinedasfollow:i.Localthresholdingisreferredwhenanimageispartitionedintosubregions,andeachsubregioncarrydifferentvalueofthreshold.Localthresholdmethodalsocalledasadaptivethresholdingschemes17-19.ii.Globalthresholdingisreferringtoassigningonlyonethresholdvaluetotheentireimage.Thresholdingtechniquesalsocanbecategorizedintotwolevels:i.Bilevelthresholding:theimageistwo(2)regionswhichareobject(black)andbackground(white).ii.Multithresholding:theimageiscomposedoffewobjectswithdifferentsurfacecharacteristicsthusneedmultiplevalueofthreshold.Thresholdingalsocanbeanalyzedasclassificationproblem,suchthatclassifiyingbilevelsegmentationofanimageintoobjectandbackground.Amongthemostcommonmethodsfoundforthresholdinginimagesegmentationarelistedasthefollowing:i.maximumentropymethod20-22ii.Otsusmethod(maximumvariance)23-26iii.k-meansclustering27-35B.EdgeDetectionTherewillbeedgeandlinedetectioninsegmentationtodivideregionsintomeaningfulinformation.Edgedetectiontechniques:Linedetection/linefinding=Houghtransform37.Houghtransformisdesignedspecificallytofindlines.Alineisacollectionofedgepoints(thatareadjacentandhavethesamedirection).TheHoughalgorithmwilltakeacollectionoffewedgepoints.Edgedetectiontechniques38-53havebeenusedasthebaseofanothersegmentationtechnique.Basically,edgedetectionisalsoanindependentprocessinimageprocessing.Edgedetection,orsometimesitiscalledasedgefindingisalsocloselyrelatedtoregiondetection.Weneedtofindtheregionboundariesfirstbeforewecanproceedtosegmentanobjectfromanimage.Thisisbecausetheedgesidentifiedbyedgedetectionarefrequentlydisconnected.Itmeansthatwehavetofindtheboundariesinordertogettheedges.Insegmentation,linedetectionisdonetodivideregionsintomeaningfulinformation.OneoflinedetectiontechniqueisHoughtransform.Houghtransformisdesignedspecificallytodetectlines.Alineisacollectionofedgepoints(thatareadjacentandhavethesamedirection).TheHoughalgorithmwilltakeacollectionoffewedgepoints.C.Region-basedimagesegmentationThistechniqueattempttoclassifyaparticularimageintoseveralregionsorclassesaccordingtothecommonpropertiesoftheimage.Therearefewpropertiesconsideredforthisprocesswhicharepatternandtexture,intensityvaluesandspectralprofilesoftheimage.Inthismethod,wewanttogrouptheregionssothateachofthepixelsintheregionwillhavesimilarvalueoftheproperties.Therearemanyrealapplicationsusedthismethodsuchasremotesensing,2Dand3Dimages54-55whiletherearevariousmodelsandalgorithmsusedforthistechniquesuchasMarkovRandomFieldModel56-60andMumford-ShahAlgorithm61-64.D.Compression-basedmethodsInthismethod,segmentationwillbedoneinawaytheimagewillbecompressedbasedonthesimilarityofthepatternsoftexturesorboundaryshapeoftheimage.Thismethodaimstominimizethelengthofthedatawheretheoptimalsegmentationcanbeachieved.TherearefewwaysonhowtocalculatethecodinglengthofthedatasuchasHuffmancodingorMDL(MinimumDescriptionLength)principle65-66,wheretheycanbefoundinpreviousstudies67-70.E.Histogram-basedmethodsHistogram-basedmethod71-76isoneofthefrequentlyusedforimagesegmentationtechniques.Inthismethod,wewillproduceaverticalandahorizontalhistogramaccordingly.Thisprocessistogetagroupofpixelsinverticalandhorizontalregionswheretheywillleadtodistinguishingthegraylevelsoftheimage.Incommon,animagewillhavetworegions:backgroundandobject.Normally,thebackgroundisassignedasonegraylevelwhilestheobject(oralsocalledassubject)isanothergraylevel.Usually,backgroundwillsecurethelargestpartoftheimagesothegraylevelofitwillhavelargerpeakinthehistogramcomparedtotheobjectoftheimage.F.Region-growingmethodsRegionGrowingandShrinking77-99techniqueuserowandcolumn(r,c)basedimagedomain.Itcanbeconsideredassubsetofclusteringmethods,butlimitedtospatialdomain.Themethodscanbe:Local:operatingonsmallneighbourhoods,orGlobal:operatingontheentireimage,orCombinationofbothG.Split-and-mergemethodsThereisanalternativeforsegmentationmethodcalledsplitandmerge100-108.Splitandmergeisalsocalledasquadtreesegmentationwhereitbasedonquadtreepartition.Thedatastructureusedinsplitandmergeiscalledquadtreewhereatreewhichhasnodesandeachnodecanhavefourchildren.Itdividesregionsthatdonotpassahomogeneitytest,andcombinesregionsthatpassthehomogeneitytest.Forthis,weproposethefollowingalgorithm,wherethepseudo-codecanbesimplifiedasthefollowing:togetwidthofy-axisoftheimagetodivideintosubregiontogetwidthofy-axisoftheimagetodivideintosubregiontodivideintosubregiontoremoveblankspacetogetsamesizeafterregionhasbeendividedTheexampleoutputcanbeviewedasFigure8Figure8.ImageoutputaftersegmentationprocessTowrapup,agoodsegmentationprocessshouldturnoutuniformandhomogeneousregionswithrespecttosomecharacteristicssuchasgraytoneortextureaswellassimpleregionswithoutmanysmallholes.Theoutputfromthesegmentationwillbeusedinthenextstagewhichischaracterrecognition.CHARACTERRECOGNITIONCharacterrecognitionisthemostimportanttaskinrecognizingtheplatenumber109.Therecognitionofcharactershasbeenaproblemthathasrecei