車牌識別外文翻譯

1、中英文翻譯Aconfigurablemethodformulti-stylelicenseplaterecognitionAutomaticlicenseplaterecognition(LPR)hasbeenapracticaltechniqueinthepastdecades.Numerousapplications,suchasautomatictollcollection,criminalpursuitandtrafficlawenforcement,havebeenbenefitedfromit.Althoughsomenoveltechniques,forexampleRFID(r

2、adiofrequencyidentification),WSN(wirelesssensornetwork),etc.,havebeenproposedforcarIDidentification,LPRonimagedataisstillanindispensabletechniqueincurrentintelligenttransportationsystemsforitsconvenienceandlowcost.LPRisgenerallydividedintothreesteps:licenseplatedetection,charactersegmentationandchar

3、acterrecognition.ThedetectionsteproughlyclassifiesLPandnon-LPregions,thesegmentationstepseparatesthesymbols/charactersfromeachotherinoneLPsothatonlyaccurateoutlineofeachimageblockofcharactersisleftfortherecognition,andtherecognitionstepfinallyconvertsgreylevelimageblockintocharacters/symbolsbypredef

4、inedrecognitionmodels.AlthoughLPRtechniquehasalongresearchhistory,itisstilldrivenforwardbyvariousarisingdemands,themostfrequentoneofwhichisthevariationofLPstyles,forexample:Appearancevariationcausedbythechangeofimagecapturingconditions.Stylevariationfromonenationtoanother.Stylevariationwhenthegovern

5、mentreleasesnewLPformat.Wesummedhemupintofourfactors,namelyrotationangle,linenumber,charactertypeandformat,aftercomprehensiveanalysesofmulti-styleLPcharacteristicsonrealdata.Generallyspeaking,anychangeoftheabovefourfactorscanresultinthechangeofLPstyleorappearanceandthenaffectthedetection,segmentatio

6、norrecognitionalgorithms.IfoneLPhasalargerotationangle,thesegmentationandrecognitionalgorithmsforhorizontalLPmaynotwork.IftherearemorethanonecharacterlinesinoneLP,additionallineseparationalgorithmisneededbeforeasegmentationprocess.Withthevariationofcharactertypeswhenweapplythemethodfromonenationtoan

7、other,theabilitytore-definetherecognitionmodelsisneeded.Whatismore,thechangeofLPstylesrequiresthemethodtoadjustbyitselfsothatthesegmentedandrecognizedcharactercandidatescanmatchbestwithanLPformat.Severalmethodshavebeenproposedformulti-nationalLPsormultiformatLPsinthepastyearswhilefewofthemcomprehens

8、ivelyaddressthestyleadaptationproblemintermsoftheabovementionedfactors.SomeofthemonlyclaimtheabilityofprocessingmultinationalLPsbyredefiningthedetectionandsegmentationrulesorrecognitionmodels.Inthispaper,weproposeaconfigurableLPRmethodwhichisadaptablefromonestyletoanother,particularlyfromonenationto

9、another,bydefiningthefourfactorsasparameters.Userscanconstrainthescopeofaparameterandatthesametimethemethodwilladjustitselfsothattherecognitioncanbefasterandmoreaccurate.SimilartoexistingLPRtechniques,wealsoprovidedetailsofdetection,segmentationandrecognitionalgorithms.Thedifferenceisthatweemphasize

10、ontheconfigurableframeworkforLPRandtheextensibilityoftheproposedmethodformultistyleLPsinsteadoftheperformanceofeachalgorithm.Inthepastdecades,manymethodshavebeenproposedforLPRthatcontainsdetection,segmentationandrecognitionalgorithms.Inthefollowingparagraphs,thesealgorithmsandLPRmethodsbasedonthemar

11、ebrieflyreviewed.LPdetectionalgorithmscanbemainlyclassifiedintothreeclassesaccordingtothefeaturesused,namelyedgebasedalgorithms,colorbasedalgorithmsandtexture-basedalgorithms.ThemostcommonlyusedmethodforLPdetectioniscertainlythecombinationsofedgedetectionandmathematicalmorphology.Inthesemethods,grad

12、ient(edges)isfirstextractedfromtheimageandthenaspatialanalysisbymorphologyisappliedtoconnecttheedgesintoLPregions.AnotherwayiscountingedgesontheimagerowstofindoutregionsofdenseedgesortodescribethedenseedgesinLPregionsbyaHoughtransformation.Edgeanalysisisthemoststraightforwardmethodwithlowcomputation

13、complexityandgoodextensibility.Comparedwithedgebasedalgorithms,colorbasedalgorithmsdependmoreontheapplicationconditions.SinceLPsinanationoftenhaveseveralpredefinedcolors,researchershavedefinedcolormodelstosegmentregionofinterestsastheLPregions.Thiskindofmethodcanbeaffectedalotbylightingconditions.To

14、winbothhighrecallandlowfalsepositiverates,textureclassificationhasbeenusedforLPdetection.InRef.Kimetal.usedanSVMtotraintextureclassifierstodetectimageblockthatcontainsLPpixels.InRef.theauthorsusedGaborfilterstoextracttexturefeaturesinmultiscalesandmultiorientationstodescribethetexturepropertiesofLPr

15、egions.InRef.ZhangusedXandYderivativefeatures,grey-valuevarianceandAdaboostclassifiertoclassifyLPandnon-LPregionsinanimage.InRefs.waveletfeatureanalysisisappliedtoidentifyLPregions.Despitethegoodperformanceofthesemethodsthecomputationcomplexitywilllimittheirusability.Inaddition,texture-basedalgorith

16、msmaybeaffectedbymulti-lingualfactors.Multi-lineLPsegmentationalgorithmscanalsobeclassifiedintothreeclasses,namelyalgorithmsbasedonprojection,binarizationandglobaloptimization.Intheprojectionalgorithms,gradientorcolorprojectiononverticalorientationwillbecalculatedatfirst.The“valleysontheprojectionre

17、sultareregardedasthespacebetweencharactersandusedtosegmentcharactersfromeachother.SegmentedregionsarefurtherprocessedbyverticalprojectiontoobtainpreciseboundingboxesoftheLPcharacters.SincesimplesegmentationmethodsareeasilyaffectedbytherotationofLP,segmentingtheskewedLPbecomesakeyissuetobesolved.Inth

18、ebinarizationalgorithms,globalorlocalmethodsareoftenusedtoobtainforegroundfrombackgroundandthenregionconnectionoperationisusedtoobtaincharacterregions.Inthemostrecentwork,localthresholddeterminationandslidewindowtechniquearedevelopedtoimprovethesegmentationperformance.Intheglobaloptimizationalgorith

19、ms,thegoalisnottoobtaingoodsegmentationresultforindependentcharactersbuttoobtainacompromiseofcharacterspatialarrangementandsinglecharacterrecognitionresult.HiddenMarkovchainhasbeenusedtoformulatethedynamicsegmentationofcharactersinLP.Theadvantageofthealgorithmisthattheglobaloptimizationwillimproveth

20、erobustnesstonoise.Andthedisadvantageisthatpreciseformatdefinitionisnecessarybeforeasegmentationprocess.CharacterandsymbolrecognitionalgorithmsinLPRcanbecategorizedintolearning-basedonesandtemplatematchingones.Fortheformerone,artificialneuralnetwork(ANN)isthemostlyusedmethodsinceitisprovedtobeableto

21、obtainverygoodrecognitionresultgivenalargetrainingset.AnimportantfactorintraininganANNrecognitionmodelforLPistobuildreasonablenetworkstructurewithgoodfeatures.SVM-basedmethodisalsoadoptedinLPRtoobtaingoodrecognitionperformancewithevenfewtrainingsamples.Recently,cascadeclassifiermethodisalsousedforLP

22、recognition.Templatematchingisanotherwidelyusedalgorithm.Generally,researchersneedtobuildtemplateimagesbyhandfortheLPcharactersandsymbols.Theycanassignlargerweightsfortheimportantpoints,forexample,thecornerpoints,inthetemplatetoemphasizethedifferentcharacteristicsofthecharacters.Invarianceoffeaturep

23、ointsisalsoconsideredinthetemplatematchingmethodtoimprovetherobustness.Thedisadvantageisthatitisdifficulttodefinenewtemplatebytheuserswhohavenoprofessionalknowledgeonpatternrecognition,whichwillrestricttheapplicationofthealgorithm.Basedontheabovementionedalgorithms,lotsofLPRmethodshavebeendeveloped.

24、However,thesemethodsaremainlydevelopedforspecificnationorspecialLPformats.InRef.theauthorsfocusonrecognizingGreekLPsbyproposingnewsegmentationandrecognitionalgorithms.ThecharactersonLPsarealphanumericswithseveralfixedformats.InRef.Zhangetal.developedalearning-basedmethodforLPdetectionandcharacterrec

25、ognition.TheirmethodismainlyforLPsofKoreanstyles.InRef.opticalcharacterrecognition(OCR)techniqueareintegratedintoLPRtodevelopgeneralLPRmethod,whiletheperformanceofOCRmaydropwhenfacingLPsofpoorimagequalitysinceitisdifficulttodiscriminaterealcharacterfromcandidateswithoutformatsupervision.Thismethodca

26、nonlyselectcandidatesofbestrecognitionresultsasLPcharacterswithoutrecoveryprocess.Wangetal.developedamethodtorecognizeLPRwithvariousviewingangles.Skewfactorisconsideredintheirmethod.InRef.theauthorsproposedanautomaticLPRmethodwhichcantreatthecasesofchangesof川umination,vehiclespeed,routesandbackgroun

27、ds,whichwasrealizedbydevelopingnewdetectionandsegmentationalgorithmswithrobustnesstothe川uminationandimageblurring.Theperformanceofthemethodisencouragingwhiletheauthorsdonotpresenttherecognitionresultinmultinationormultistyleconditions.InRef.theauthorsproposeanLPRmethodinmultinationalenvironmentwithc

28、haractersegmentationandformatindependentrecognition.Sincenorecognitioninformationisusedincharactersegmentation,falsesegmentedcharactersfrombackgroundnoisemaybeproduced.Whatismore,therecognitionmethodisnotalearning-basedmethod,whichwilllimititsextensibility.InRef.Mposeagenerativerecognitionmethod.Gen

29、erativemodels(GM)areproposedtoproducemanysyntheticcharacterswhosestatisticalvariabilityisequivalent(foreachclass)tothatshowedbyrealsamples.Thusasuitablestatisticaldescriptionofalargesetofcharacterscanbeobtainedbyusingonlyalimitedsetofimages.Asaresult,theextensionabilityofcharacterrecognitionisimprov

30、ed.ThismethodmainlyconcernsthecharacterrecognitionextensibilityinsteadofwholeLPRmethod.FromthereviewwecanseethatLPRmethodinmultistyleLPRwithmultinationalapplicationisnotfullyconsidered.LotsofexistingLPRmethodscanworkverywellinaspecialapplicationconditionwhiletheperformancewilldropsharplywhentheyaree

31、xtendedfromoneconditiontoanother,orfromseveralstylestoothers.多類型車牌識別配置的方法自動車牌識別（LPR）在過去的幾十年中的實(shí)用技術(shù)。許多應(yīng)用，如自動收費(fèi)，犯罪的追求和交通執(zhí)法，已從中受益。雖然一些新技術(shù)，如RFID（無線射頻識別），WSN（無線傳感器網(wǎng)絡(luò)），等，已提出了汽車身份識別，車牌圖像數(shù)據(jù)仍因其方便、成本低，在目前的智能交通系統(tǒng)不可缺少的技術(shù)。車牌識別系統(tǒng)一般分為三個步驟：車牌定位，字符分割和字符識別。檢測步驟大致分類LP和非LP區(qū)域分割步驟，將符號/字符從彼此在一個LP,只有準(zhǔn)確的輪廓，每個字符圖像塊左為識別和識別步驟，最

32、后將灰度圖像塊轉(zhuǎn)換成字符/符號通過預(yù)定義的識別模型。雖然車牌識別技術(shù)有著很長的研究歷史，它仍然是推動各種要求而產(chǎn)生的，最常見的一個是LP風(fēng)格的變化，例如：（1）通過圖像采集條件的變化引起的外觀變化。風(fēng)格的變化從一個國家到另一個。風(fēng)格的變化時，政府發(fā)布新的LP格式。我們將其總結(jié)為四個因素，即旋轉(zhuǎn)角度，線數(shù)，性格類型和格式，在對實(shí)際數(shù)據(jù)的多樣式的LP特征綜合分析。一般來說，上述四個因素的任何變化都會導(dǎo)致LP的風(fēng)格或外表的變化進(jìn)而影響檢測，分割和識別算法。如果LP有一個大的旋轉(zhuǎn)角度，水平LP分割和識別算法可能不工作。如果有一個以上的在一個LP的特征線，更多的線分離算法分割處理前需要。與人的性格類型的

33、變化時，我們采用的方法從一個國家到另一個，有能力重新定義識別模型是必要的。更甚的是，LP風(fēng)格的變化需要調(diào)整的方法本身，分割和識別候選字符可以匹配最好用一個LP格式。已經(jīng)提出了幾種方法，近年來跨國LPS或LPS多而很少全面解決上述因素的風(fēng)格適應(yīng)問題。他們中的一些人只要求處理跨國LPS的能力通過重新定義的檢測和分割規(guī)則或識別模型。在本文中，我們提出了一個可配置的車牌識別方法是從一個到另一個適合的風(fēng)格，特別是從一個國家到另一個，通過定義四個因素作為參數(shù)。用戶可以約束的參數(shù)范圍，同時該方法將自我調(diào)整，這樣可以更快、更準(zhǔn)確的識別。類似于現(xiàn)有的車牌識別技術(shù)，我們還提供詳細(xì)的檢測，分割和識別算法。不同的是，

34、我們強(qiáng)調(diào)了車牌識別和可擴(kuò)展性的方法而不是multistyleLPS各算法性能的可配置的框架。在過去的幾十年中，已經(jīng)提出了許多方法用于車牌識別包含檢測，分割和識別算法。在下面的段落中，這些算法和車牌識別方法的基礎(chǔ)上，簡要回顧。低壓檢測算法主要可按特征分為三類，即edgebased算法，基于顏色特征的算法和基于紋理的算法。LP檢測最常用的方法是邊緣檢測和數(shù)學(xué)形態(tài)學(xué)的組合。在這些方法中，梯度（邊）是第一個從圖像中提取和隨后的形態(tài)空間分析應(yīng)用于邊緣連接到低壓區(qū)域。另一種方法是計數(shù)的邊緣在圖像行發(fā)現(xiàn)密集的邊緣地區(qū)或描述密集的邊緣在LP地區(qū)的Hough變換。邊緣分析是最簡單的方法具有較低的計算復(fù)雜度和良好

35、的可擴(kuò)展性。與edgebased算法相比，基于顏色特征的算法更依賴于應(yīng)用條件。由于LPS中的國家往往有幾個預(yù)定義的顏色，研究人員已經(jīng)定義的顏色模型的分割區(qū)域的利益為低壓區(qū)。這種方法可以通過照明條件影響很大。贏得了較高的召回率和較低的誤報率，紋理分類已被用于低壓檢測。在參考這種方法可以通過照明條件影響很大。贏得了較高的召回率和較低的誤報率，紋理分類已被用于低壓檢測?；返热嗽谖墨I(xiàn)。使用SVM分類器來檢測圖像塊的紋理包含LP像素。在參考文獻(xiàn)作者使用Gabor濾波器提取紋理特征的多尺度、multiorientations描述LP區(qū)域的紋理特性。在參考文獻(xiàn)采用張X和Y的衍生功能，灰度值的方差和AdaB

36、oost分類器，對圖像中的LP和非LP區(qū)域進(jìn)行分類，在文獻(xiàn)。小波特征分析方法識別低壓區(qū)。盡管這些方法的計算復(fù)雜性限制了他們的可用性，性能良好。此外，基于紋理的算法可以通過多語言因素的影響。多線的LP分割算法可分為三類，即算法的基礎(chǔ)上投影，二值化和全局優(yōu)化。在投影算法，梯度或彩色投影在垂直方向?qū)⑾扔嬎恪！肮取痹谕队敖Y(jié)果作為特征和用于分割字符之間的空間。分割區(qū)域的垂直投影的進(jìn)一步處理以獲得精確的包圍盒的LP角色。從簡單的分割方法很容易通過LP的旋轉(zhuǎn)的影響，對傾余的LP成為亟待解決的關(guān)鍵問題。在二值化算法，全局或局部的方法經(jīng)常被用來從背景前景并獲得區(qū)域連接操作是用來獲取字符區(qū)域。在最近的工作中，局部閾值測定和滑動窗口技術(shù)的開發(fā)，以提高