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英文原文1.IntroductionNowadayslicenseplaterecognitionbecomesakeytechniquetomanyautomatedtransportsystemssuchasroadtrafficmonitoring,automaticpaymentoftollsonhighwaysorbridgesandparkinglotsaccesscontrol.Licenseplatelocationisanessentialandimportantstageinthistechnique,andithasreceivedconsiderableattention.Researchershavefoundmanydiversemethodsoflicenseplatelocation.RodolfoandStefano(2000)devisedamethodbasedonvectorquantization(VQ).VQimagerepresentationisaquadtreerepresentationbythespecificcodingmechanism,anditcangiveasystemsomehintsaboutthecontentsofimageregions,andsuchinformationboostslocationperformance.Parketal.(1999)usedneuralnetworkstolocatelicenseplate.Neuralnetworkscanbeusedasfiltersforanalyzingsmallwindowsofanimageanddecidingwhethereachwindowcontainsalicenseplate,andtheirinputsareHSIvalues；apost-processorcombinesthesefilteredimagesandlocatestheboundingboxesoflicenseplatesintheimage.Besidesneuralnetworks,otherfiltershavebeenconsideredtoo.Forexample,someauthorsusedlinesensitivefilterstoextracttheplateareas.Licenseplatesareidentifiedasimageareaswithhighdensityofratherthindarklinesorcurves.Therefore,localizationishandledlookingforrectangularregionsintheimagecontainingmaximaofresponsetotheselinefilters,whichiscomputedbyacumulativefunction(Luisetal.,1999).Platecharacterscanbedirectidentifiedbyscanningthroughtheinputimageandlookingforportionsoftheimagethatwerenotlinkedtootherpartsoftheimage.Ifanumberofcharactersarefoundtobeinastraightline,theymaymakeupalicenseplate(Limetal.,1998).FuzzylogichasbeenappliedtotheproblemoflocatinglicenseplatebyZimicetal.(1997).Theauthorsmadesomeintuitiverulestodescribethelicenseplate,andgavesomemembershipfunctionsforthefuzzysetsbrightanddark,brightanddarksequencetogetthehorizontalandverticalplatepositions.Butthismethodissensitivetothelicenseplatecolorandbrightnessandneedsmuchprocessingtime.UsingcolorfeaturestolocatelicenseplatehasbeenstudiedbyZhuetal.(2002)andWeietal.(2001),butthesemethodsarenotrobustenoughtothedifferentenvironments.Edgefeaturesofthecarimageareveryimportant,andedgedensitycanbeusedtosuccessfullydetectanumberplatelocationduetothecharacteristicsofthenumberplate.Mingetal.(1996)developedamethodtoimprovetheedgeimagebyeliminatingthehighestandlowestportionsoftheedgedensitytosimplifythewholeimage.Butsomeoftheplateregionidentitywillbelostinthismethod.Thispaperfurtherresearchesthesubjectoflicenseplatelocation.Therectanglelicenseplatecontainsrichedgeandtextureinformation,soweconsideritinitsedgeimagebutverydifferenttoMingetal.(1996).Wefirstenhancetheoriginalcarimagetoboostuptheplatearea,thenextracttheverticaledgeimageusingSobeloperator,andthenremovethebackgroundcurvesandnoiseintheedgeimage,andfinallyslidearectanglewindowtosearchtheplateintheresidualimageandsegmentitoutfromtheoriginalcarimage.Section2describesourmethodoflicenseplatelocation,anditcontainsfourparts:imageenhancement,verticaledgeextraction,backgroundcurveandnoiseremoving,platesearchandsegmentation.ExperimentswiththreesetsofcarimagesareperformedinSection3.Section4givesthediscussionandconclusions.1.TheproposedmethodforlicenseplatelocationAlltheinputcarimageshave384·288pixelsand256graylevels,andanexampleimageisgiveninFig.1.Thelicenseplateofthecarconsistsofseveralcharacters(suchasLatinletters,Arabicnumerals,etc.),sotheplateareacontainsrichedgeinformation.Butsometimesthebackgroundofthecarimageholdsmuchedgeinformationtoo.Therearetwofactsthatattractourattention:oneisthatthebackgroundareasaroundthelicenseplatemainlyincludesomehorizontaledges；theotheristhattheedgesinthebackgroundaremainlylongcurvesandrandomnoises,whereastheedgesintheplateareaclustertogetherandproduceintensetexturefeature.Ifonlytheverticaledgesareextractedfromthecarimage(althoughtheplatewilllosealittlehorizontaledgeinformation,thislittlelossistobevaluable)andmostofthebackgroundedgesareremoved,theplateareawillbeisolatedoutdistinctlyinthewholeedgeimage.Thusweproposetolocatethelicenseplateinitsverticaledgeimageasthefollowingfourstages.2.1.ImageenhancementInFig.1,thegradientsinthelicenseplateareaaremuchlowerthanthoseinthecontourareasofthecar,whichiscausedbythecarshadowinthedazzlingsunshine.Thecarimagescapturedinthegloomydaysordimnightsoftenbringoutweakgradientsinplateareastoo.Afewverticaledgeswillappearintheplateareas,ifweextractedgeimagesdirectlyfromthesecarimages.Thereforeitisimportanttoenhancethecarimagesfirstly.Thelocalareasthatneedtobeenhancedinacarimagehavelowvariances.HereweuseIi,jtodenotetheluminanceofthepixelPi,j(row:06i<288,column:06j<384)inthecarimage,anduseI1i；jtodenotetheluminanceintheenhancedimage.WeletIi,jandI1i；jsatisfyEq.(1),whereWi,jisawindowcenteredonpixelPi,j,IWi；jandrWi；jarethemeanluminanceandstandarddeviationofthepixelsinthewindowWi,j,I0andr0aretheexpectedmeanandstandarddeviation,respectively.0,0,1)(,IIIIjijiwjiwji（1）Inordertorepresentthelocalinformationbetter,thesizeofthewindowshouldbesmallerthantheestimatedsizeoftheplate.Inthispaper,weselecta48·36rectangleasthewindowWi,jandthus8·8windowscancoveroverthewhole384·288carimage.LetI0beequaltoIWi；jandr0beaconstantindependentofpixelPi,j.NowweneedtoknowthevaluesIWi；jandrWi；jateachpixel.Computingoutallthevaluesisnotadvisable,andwecanusethebilinearinterpolationalgorithmtogetthem.Firstwecutthecarimageinto8·8blocksequably；andthencomputeouttheIWi；jandrWi；jvaluesatthevertexesofblocks,wherei=36m,j=48n,m,n=0,1,2,.,8；finallycomputeouteveryIWi；jandrWi；jbythebilinearinterpolationEqs.(2)and(3)(Fig.2),where36m6i<36(m+1),48n6j<48(n+1),cx=(j48n)/48,andcy=(i-36m)/36.)1()1)(1(,DBAjiwxwDxywxwxywIcIccIcIccI（2）)1()1)(1(,DCBAjiwxwxywxwxywcccccc（3）IfWi；jisclosetozero(suchasonlyadarkorbrightarea),Eq.(1)willgiveoutalargevalue.Butweshouldnotenhancesuchalocalarea.IfWi；jishighenough(forexampleWi；j60),theenhancementisunnecessarytoo.SotheenhancementEq.(1)isimprovedintoEq.(4)inpractice.jijijiwwjiwjiIIIfI,)()(,（4）wheref（rWi；j）isanenhancementcoefficient(showninFig.3)definedbyEq.(5).MostWi；jsoftheplateareaswhichneedenhancedarearound20.Soweletthefunctionfbeequalto1whenrWi；j=0orrWi；j60,andbeequalto3(as20·3=60)whenrWi；j=20.60160201)20(1600232001)20(40023)(,22jijijijijijiWWWWWWifififf（5）TheenhancedcarimageisshowninFig.4.Andwecanseethatthelicenseplateregionhasbeenstrengthened.Iftheplateiswellilluminatedandtheimageisinbalance,theprocesswillnotchangethecontrastoftheplate(f（wi,j=1,ifwi,j60）.2.2.VerticaledgeextractionWeselecttheverticalSobeloperator(inFig.5)todetecttheverticaledges,becausethesimpleoperatorcostsusalittlecomputationaltime.ConvolvethecarimagewiththisSobeloperatortogettheverticalgradientimage.Computethemeanoftheabsolutegradientvaluesintheimageandmultiplyitbyacoefficientasathreshold(forexample4Grad),orcomputethegradienthistogramandfindagradientatacertainpercentage(forexample75%)ofthegradientdistributionasathreshold.Usethisthresholdandapplynonmaximumsuppressioninhorizontaldirectioninthegradientimage,andwegettheverticalSobeledgeimageshowninFig.6.2.3.BackgroundcurveandnoiseremovingFromFig.6,wecanseethattherearemanylongbackgroundcurvesandshortrandomnoiseedgesintheverticaledgeimagebesidesthelicenseplateedges.Thesebackgroundandnoiseedgesmayinterfereinthelicenseplatelocation.Wehaveproposedasimplealgorithmtoremovethemfromtheedgeimage.Thisalgorithmonlyrequiresustoscantheedgeimageforthreetimes.Thefirstscanwillrecordtheedgelengthsawayfromthetop(orleft)startpoints.Andthesecondscanwillrecordtheedgelengthsawayfromthebottom(orright)endpoints.Andthelastscanwilladdupthetwokindsoflengthstodenotetheactualedgelengths；iftheedgepointhasaverylong(backgroundcurve)orveryshort(noiseedge)actualedgelength,thentheedgepointwillberemovedfromtheedgeimage.Beforedescribethealgorithmbelow,weneedtointroducesomesymbolsused:Edenotestheedgeimage(ifpixelPi,jisanedgepoint,Ei,j=1,elseEi,j=0)；MandNarebothmatrixeswiththesamesizeasE；Tlong,whichhasrelationtotheestimatedheightofthelicenseplate,andTshort,whichisshorterthanmostofthelengthsoftheplateedges,aretwothresholdsofedgelengths.1.initializeMandNtozeromatrixes；2.foreachrowifromtop-to-bottomdoforeachcolumnjfromleft-to-rightdoif(Ei,j=1)if(Ei1,j1+Ei1,j+Ei1,j+1+Ei,j1>0)Mi,j=maxMi1,j1,Mi1,j,Mi1,j+1,Mi,j1+1；elseMi,j=maxMi2,j1,Mi2,j,Mi2,j+1,Mi1,j2,Mi1,j+2,Mi,j2+1；endendendend3.foreachrowifrombottom-to-topdoforeachcolumnjfromright-to-leftdoif(Ei,j=1)if(Ei+1,j1+Ei+1,j+Ei+1,j+1+Ei,j+1>0)Ni,j=maxNi+1,j1,Ni+1,j,Ni+1,j+1,Ni,j+1+1；elseNi,j=maxNi+2,j1,Ni+2,j,Ni+2,j+1,Ni+1,j2,Ni+1,j+2,Ni,j+2+1；endendendend4.foreachrowifromtop-to-bottomdoforeachcolumnjfromleft-to-rightdoif(Ei,j=1)if(Mi,j+Ni,j>TlongkMi,j+Ni,j<Tshort)Ei,j=0；