文献翻译——图像处理操作的层次结构

第1页外文文献资料HierarchyimageprocessingopreationImageprocessingisnotaonestepprocess.WeareabletodistinguishbetweenseveralstepswhichmustbeperformedoneaftertheotheruntilwecanextractthedataofinterestfromtheobservedsceneInthiswayahierarchicalprocessingschemeisbuiltupassketchedinFigThefiguregivesanoverviewofthedifferentphasesofimageprocessing.Imageprocessingbeginswiththecaptureofanimagewithasuitable，notnecessarilyoptical，acquisitionsystem.Inatechnicalorscientificapplication,wemaychoosetoselectanappropriateimagingsystem.Furthermore，cansetuptheilluminationsystem,choosethebestwavelengthrange，andselectotheroptionstocapturetheobjectfeatureofinterestinthebestwayinanimageOncetheimageissensed，itmustbebroughtintoaformthatcanbetreatedwithdigitalcomputers.Thisprocessiscalleddigitization.Withtheproblemsoftrafficaremoreandmoreserious.ThusIntelligentTransportSystem(ITS)comesout.Thesubjectoftheautomaticrecognitionoflicenseplateisoneofthemostsignificantsubjectsthatareimprovedfromtheconnectionofcomputervisionandpatternrecognition.TheimageimputedtothecomputerisdisposedandanalyzedinordertolocalizationthepositionandrecognitionthecharactersonthelicenseplateexpressthesecharactersintextstringformThelicenseplaterecognitionsystem(LPSR)hasimportantapplicationinITS.InLPSR,thefirststepisforlocatingthelicenseplateinthecapturedimagewhichisveryimportantforcharacterrecognition.Therecognitioncorrectionrateoflicenseplateisgovernedbyaccuratedegreeoflicenseplatelocation.Inthispaper,severalofmethodsinimagemanipulationarecomparedandanalyzed,thencomeouttheresolutionsforlocalizationofthecarplate.Theexperiencesshowthatthegoodresulthasbeengotwiththesemethods.Themethodsbasedonedgemapandfrequencyanalysisisusedintheprocessofthelocalizationofthelicenseplate,thatistosay,extractingthecharacteristicsofthelicenseplateinthecarimagesafterbeingcheckedupfortheedge,andthenanalyzingandprocessinguntiltheprobablyareaoflicenseplateisextracted.Theautomatedlicenseplatelocationisapartoftheimageprocessing,itsalsoanimportant第2页partintheintelligenttrafficsystemItisthekeystepintheVehicleLicensePlateRecognition(LPR).Amethodfortherecognitionofimagesofdifferentbackgroundsanddifferentilluminationsisproposedinthepaper.Theupperandlowerbordersaredeterminedthroughthegrayvariationregulationofthecharacterdistribution.Theleftandrightbordersaredeterminedthroughtheblack-whitevariationofthepixelsineveryrow.ThefirststepsofdigitalprocessingmayincludeanumberofdifferentoperationsandareknownasimageprocessingIfthesensorhasnonlinearcharacteristics,theseneedtobecorrected.Likewise,brightnessandcontrastoftheimagemayrequireimprovementCommonly，too,coordinatetransformationsareneededtorestoregeometricaldistortionsintroducedduringimageformationRadiometricandgeometriccorrectionsareelementarypixelprocessingoperationsItmaybenecessarytocorrectknowndisturbancesintheimage，forinstancecausedbyadefocusedoptics，motionblur，errorsinthesensor，orerrorsinthetransmissionofimagesignalsWealsodealwithreconstructiontechniqueswhicharerequiredwithmanyindirectimagingtechniquessuchastomographythatdelivernodirectimage.AwholechainofprocessingstepsisnecessarytoanalyzeandidentifyobjectsFirst，adequatefilteringproceduresmustbeappliedinordertodistinguishtheobjectsofinterestfromotherobjectsandthebackgroundEssentially，fromanimage（orseveralimages），oneormorefeatureimagesareextractedThebasictoolsforthistaskareaveragingandedgedetectionandtheanalysisofsimpleneighborhoodsandcomplexpatternsknownastextureinimageprocessingAnimportantfeatureofanobjectisalsoitsmotionTechniquestodetectanddeterminemotionarenecessaryThentheobjecthastobeseparatedfromthebackgroundThismeansthatregionsofconstantfeaturesanddiscontinuitiesmustbeidentifiedThisprocessleadstoalabelimageNowthatweknowtheexactgeometricalshapeoftheobject，wecanextractfurtherinformationsuchasthemeangrayvalue，thearea，perimeter，andotherparametersfortheformoftheobject3TheseparameterscanbeusedtoclassifyobjectsThisisanimportantstepinmanyapplicationsofimageprocessing，asthefollowingexamplesshow：Inasatelliteimageshowinganagriculturalarea，wouldliketodistinguishfieldswithdifferentfruitsweandobtainparameterstoestimatetheirripenessortodetectdamagebyparasitesTherearemanymedicalapplicationswheretheessentialproblemistodetectpathologi-alchangesAclassicexampleistheanalysisofaberrationsinchromosomesCharacterrecognitioninprintedandhandwrittentextisanotherexamplewhichhasbeenstudiedsince第3页imageprocessingbeganandstillposessignificantdifficultiesYouhopefullydomore，namelytrytounderstandthemeaningofwhatyouarereadingThisisalsothefinalstepofimageprocessing，whereoneaimstounderstandtheobservedsceneWeperformthistaskmoreorlessunconsciouslywheneverweuseourvisualsystemWerecognizepeople，wecaneasilydistinguishbetweentheimageofascientificlabandthatofalivingroom，wewatchthetraffictocrossastreetsafelyandWealldothiswithoutknowinghowthevisualsystemworksForsometimesnow，imageprocessingandcomputer-graphicshavebeentreatedastwodifferentareasKnowledgeinbothareashasincreasedconsiderablyandmorecomplexproblemscannowbetreatedComputergraphicsisstrivingtoachievephotorealisticcomputer-generatedimagesofthree-dimensionalscenes，whileimageprocessingistryingtoreconstructonefromanimageactuallytakenwithacameraInthissense，imageprocessingperformstheinverseproceduretothatofcomputergraphicsWestartwithknowledgeoftheshapeandfeaturesofanobjectatthebottomofFig.andworkupwardsuntilwegetatwo-dimensionalimageTohandleimageprocessingorcomputergraphics，webasicallyhavetoworkfromthesameknowledgeWeneedtoknowtheinteractionbetweenilluminationandobjects，howathree-dimensionalsceneisprojectedontoanimageplane，etcTherearestillquiteafewdifferencesbetweenanimageprocessingandagraphicsworkstationButwecanenvisagethat，whenthesimilaritiesandinterrelationsbetweencomputergraphicsandimageprocessingarebetterunderstoodandtheproperhardwareisdeveloped，wewillseesomekindofgeneral-purposeworkstationinthefuturewhichcanhandlecomputergraphicsaswellasimageprocessingtasks.Theadventofmultimedia,i.e.，theintegrationoftext，images，sound,andmovies,willfurtheracceleratetheunificationofcomputergraphicsandimageprocessing.InJanuary1980ScientificAmericanpublishedaremarkableimagecalledPlume2，thesecondofeightvolcaniceruptionsdetectedontheJovianmoonbythespacecraftVoyager1on5March1979ThepicturewasalandmarkimageininterplanetaryexplorationthefirsttimeaneruptingvolcanohadbeenseeninspaceItwasalsoatriumphforimageprocessing.Satelliteimageryandimagesfrominterplanetaryexplorershaveuntilfairlyrecentlybeenthemajorusersofimageprocessingtechniques，whereacomputerimageisnumericallymanipulatedtoproducesomedesiredeffect-suchasmakingaparticularaspectorfeatureintheimagemorevisible.ImageprocessinghasitsrootsinphotoreconnaissanceintheSecondWorldWarwhere第4页processingoperationswereopticalandinterpretationoperationswereperformedbyhumanswhoundertooksuchtasksasquantifyingtheeffectofbombingraidsWiththeadventofsatelliteimageryinthelate1960s，muchcomputer-basedworkbeganandthecolorcompositesatelliteimages，sometimesstartlinglybeautiful,havebecomepartofourvisualcultureandtheperceptionofourplanetLikecomputergraphics，itwasuntilrecentlyconfinedtoresearchlaboratorieswhichcouldaffordtheexpensiveimageprocessingcomputersthatcouldcopewiththesubstantialprocessingoverheadsrequiredtoprocesslargenumbersofhigh-resolutionimagesWiththeadventofcheappowerfulcomputersandimagecollectiondeviceslikedigitalcamerasandscanners，wehaveseenamigrationofimageprocessingtechniquesintothepublicdomainClassicalimageprocessingtechniquesareroutinelyemployedbygraphicdesignerstomanipulatephotographicandgeneratedimagery，eithertocorrectdefects，changecolorandsoonorcreativelytotransformtheentirelookofanimagebysubjectingittosomeoperationsuchasedgeenhancement.ArecentmainstreamapplicationofimageprocessingisthecompressionofimageseitherfortransmissionacrosstheInternetorthecompressionofmovingvideoimagesinvideotelephonyandvideoconferencingVideotelephonyisoneofthecurrentcrossoverareasthatemploybothcomputergraphicsandclassicalimageprocessingtechniquestotrytoachieveveryhighcompressionratesAllthisispartofaninexorabletrendtowardsthedigitalrepresentationofimagesIndeedthatmostpowerfulimageformofthetwentiethcenturytheTVimageisalsoabouttobetakenintothedigitaldomainImageprocessingischaracterizedbyalargenumberofalgorithmsthatarespecificsolutionstospecificproblemsSomearemathematicalorcontext-independentoperationsthatareappliedtoeachandeverypixelForexample，wecanuseFouriertransformstoperformimagefilteringoperationsOthersare“algorithmic”wemayuseacomplicatedrecursivestrategytofindthosepixelsthatconstitutetheedgesinanimageImageprocessingoperationsoftenformpartofacomputervisionsystemTheinputimagemaybefilteredtohighlightorrevealedgespriortoashapedetectionusuallyknownaslow-leveloperationsIncomputergraphicsfilteringoperationsareusedextensivelytoavoidabasingorsamplingartifacts.第5页中文翻译稿图像处理操作的层次结构图像处理不是一步就能完成的过程。可将它分成诸多步骤，必须一个接一个地执行这些步骤，直到从被观察的景物中提取出有用的数据。依据这种方法，一个层次化的处理方案如图12-1所示，该图给出了图像处理不同阶段的概观。图像处理首先是以适当的但不一定是光学的采集系统对图像进行采集。在技术或科学应用中，可以选择一个适当的成像系统。此外，可以建立照明系统，选择最佳波长范围，以及选择其他方案以便用最好的方法在图像中获取有用的对象特征。一旦图像被检测到，必须将其变成数字计算机可处理的形式，这个过程称之为数字化。随着交通问题的日益严重，智能交通系统应运而生。汽车牌照自动识别系统是近几年发展起来的计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。课题的目的是对摄像头获取的汽车图像进行预处理,确定车牌位置,提取车牌上的字符串，并对这些字符进行识别处理,用文本的形式显示出来。车牌自动识别技术在智能交通系统中具有重要的应用价值。在车牌自动识别系统中，首先要将车牌从所获取的图像中分割出来，这是进行车牌字符识别的重要步骤，定位准确与否直接影响车牌识别率。本文在对各种车辆图像处理方法进行分析、比较的基础上,提出了车牌预处理、车牌粗定位和精定位的方法，并且取得了较好的定位结果。车牌定位采取的是边缘检测的频率分析法。从经过边缘提取后的车辆图像中提取车牌特征,进行分析处理,从而初步定出车牌的区域，再利用车牌的先验知识和分布特征对车牌区域二值化图像进行处理，从而得到车牌的精确区域。汽车牌照的自动定位是图像处理的一种，也是智能交通系统中的重要组成部分之一，是实现车牌识别(LPR)系统的关键。针对不同背景和光照条件下的车辆图像，提出了一种基于灰度图像灰度变化特征进行车牌定位的方法。依据车牌中字符的灰度变化以峰、谷规律分布确定车牌上下边界，对扫描行采用灰度跳变法确定车牌左右边界。数字化处理的第一步包含了一系列不同的操作并被称之为图像处理。如果传感器具有非线性特性，就必须予以校正，同样，图像的亮度和对比度也需要改善。通常，还需要进行坐标变换以消除在成像时产生的几何畸变。辐射度校正和几何校正是最基本的像素处理操作。第6页在图像中，对已知的干扰进行校正也是不可少的，比如由于光学聚焦不准，运动模糊，传感器误差以及图像信号传输误差所引起的干扰。在此还要涉及图像重构技术，它需要许多间接的成像技术，比如不直接提供图像的X射线断层技术等。一套完整的处理步骤对于物体的分析和识别是必不可少的。首先，应该采用适当的过滤技术以便从其他物体和背景中将所感兴趣的物体区分出来。实质上就是从一幅图像（或者数幅图像）中抽取出一幅或几幅特征图像。要完成这个任务最基本的工具就是图像处理中所使用的求均值和边缘检测、简单的相邻像素分析，以及复杂的被称为材质描述的模式分析。物体的一个重要特性就是它的运动性。检测和确定物体运动性的技术是必不可少的。随后，该物体必须从背景中分离出来，这就意味着具有同样特性和不同特性的区域必须被识别出来。这个过程产生出标志图像。既然已经知道了物体精确的几何形状，就可以抽取诸如平均灰度值、区域、边界以及形成物体的其他参数等更多的信息。这些参数可用来对物体进行分类，这是许多图像处理应用中至关重要的一步，比如下面一些应用：在一个显示农业地区的卫星图像中，想要区别出不同的果树，并获取参数以估算出成熟情况并监测害虫情况；在许多的医学应用中，最基本的问题是检查病理变化，最典型的应用就是染色体畸变分析；印刷体和手写体识别是另一个例子，图像处理一出现，人们就开始对它进行着研究，现在依然困难重重。人们希望能了解得更多一些，也就是试图理解所读到的内容。这也是图像处理的最后一个步骤，即理解所观察到的景象。当我们使用视觉系统时，实际上已或多或少无意识地在执行这个任务。我们能识别不同的人，可以很轻易地区分出实验室和起居室，可以观察车流以便安全地穿行马路。我们完成这样的任务而并不了解视觉系统工作的奥秘。长久以来图像处理和计算机图形学被看做两个不同的领域。现在，人们在这两个领域中的知识都有了极大的提高，并可以解决许多复杂的问题。计算机图形学正在努力使三维景物的计算机图像达到照片级效果。而图像处理则试图对用照相机实际拍摄的图像进行重构。从这个意义上讲，图像处理完成的是与计算机图形技术相反的过程。但从有关物体的形状和特性知识开始（如图12-1的底部所示），向上直到获得一个二维图像要运