圖像處理-畢設(shè)論文外文翻譯(翻譯+原文)

Imageprocessingisnotaonestepprocess．WeareabletodistinguishbetweenseveralstepswhichmustbeperformedoneaftertheotheruntilwecanextractthedataofinterestfromtheobservedsceneprocessingschemeisbuiltupassketchedinFigofthedifferentphasesofimageprocessing.．Inthiswayahierarchical．ThefiguregivesanoverviewWiththeproblemsoftrafficaremoreandmoreserious.ThusIntelligentTransportSystem(ITS)comesout.Thesubjectoftheautomaticrecognitionoflicenseplateisoneofthemostsignificantsubjectsthatareimprovedfromtheconnectionofcomputervisionandpatternrecognition.TheimageimputedtothecomputerisdisposedandanalyzedinordertolocalizationthepositionandrecognitionthecharactersonthelicenseplateexpressthesecharactersintextstringformThelicenseplaterecognitionsystem(LPSR)hasimportantapplicationinITS.InLPSR,thefirststepisforlocatingthelicenseplateinthecapturedimagewhichisveryimportantforcharacterrecognition.Therecognitioncorrectionrateoflicenseplateisgovernedbyaccuratedegreeoflicenseplatelocation.Inthispaper,severalofmethodsinimagemanipulationarecomparedandanalyzed,thencomeouttheresolutionsforlocalizationofthecarplate.Theexperiencesshowthatthegoodresulthasbeengotwiththesemethods.Themethodsbasedonedgemapandfrequencyanalysisisusedintheprocessofthelocalizationofthelicenseplate,thatistosay,extractingthecharacteristicsofthelicenseplateinthecarimagesafterbeingcheckedupforuntiltheprobablyareaoflicenseimageprocessing,it’s．ItisthekeystepintheAwholechainofprocessingstepsisnecessarytoanalyzeandidentifyobjects．First，adequatefilteringproceduresmustbeappliedinordertodistinguishtheobjectsofinterestfromotherobjectsandthebackgroundEssentiallyfroman（orseveralimagesoneormorefeatureimagesareextracted．Thebasictoolsforthistaskareaveragingandedgedetectionandtheanalysisofsimpleneighborhoodsandcomplexpatternsknownastextureinimageprocessing．Animportantfeatureofanobjectisalsoitsmotion．Techniquestodetectanddeterminemotionarenecessary．Thentheobjecthastobeseparatedfromthebackground．Thismeansthatregionsofconstantfeaturesanddiscontinuitiesmustbeidentified．Thisprocessleadstoa，weobtainparameterstoestimatetheirripenessortodetectdamagebyparasitesTherearemanymedicalapplicationswheretheessentialproblemistodetectpathologi-alchanges．Aclassicexampleistheanalysisofaberrationsinchromosomes．Characterrecognitioninprintedandhandwrittentextisanotherexamplewhichhasbeenstudiedsinceimageprocessingbeganandstillposessignificantdifficulties．a(chǎn)rereading．ThisisalsothefinalstepofimageprocessingwewatchthetraffictocrossastreetsafelyWealldothiswithoutknowinghowthevisualsystemworks．Forsometimesnow，imageprocessingandcomputer-graphicshavebeentreatedastwodifferentareasimage．Tohandleimageprocessingorcomputergraphicsworkfromthesameknowledge．Weneedtoknowtheinteractionbetweenilluminationandobjects，howathree-dimensionalsceneisprojectedontoanimageplane，etc．Therearestillquiteafewdifferencesbetweenanimageprocessingandagraphicsworkstation．Butwecanenvisagethat，whenthesimilaritiesandinterrelationsbetweencomputergraphicsandimageprocessingarebetterunderstoodandtheproperhardwareisdeveloped，wewillseesomekindofgeneral-purposeworkstationinthefuturewhichcanhandlecomputergraphicsaswellasimageprocessingtasks[5]．Theadventofmultimedia，i.e.，theintegrationoftext，images，sound，andmovies，willfurtheracceleratetheunificationofcomputergraphicsandimageprocessing.InJanuary1980ScientificAmericanpublishedaremarkableimagecalledPlume2，thesecondofeightvolcaniceruptionsdetectedontheJovianmoonbythespacecraftVoyager1on5March1979Thepicturewasalandmarkimageininterplanetaryexploration—thefirsttimeaneruptingvolcanohadbeenseeninspace．Itwasalsoatriumphforimageprocessing．Satelliteimageryandimagesfrominterplanetaryexplorershaveuntilfairlyrecentlybeenthemajorusersofimageprocessingtechniquesofbombingraids．Withtheadventofsatelliteimageryinthelate1960scomputer-basedworkbeganandthecolorcompositesatelliteimagessometimescomputersandimagecollectiondeviceslikedigitalcamerasandscannershaveseenamigrationofimageprocessingtechniquesintothepublicdomain．Classicalimageprocessingtechniquesareroutinelyemployedbygraphicdesignerstomanipulatephotographicandgeneratedimagery，eithertoArecentmainstreamapplicationofimageprocessingisthecompressionofimages—eitherfortransmissionacrosstheInternetorthecompressionofmovingvideoimagesinvideotelephonyandvideoconferencing．Videotelephonyisoneofthecurrentcrossoverareasthatemploybothcomputergraphicsandclassicalimageprocessingtechniquestotrytoachieveveryhighcompressionrates．AllthisispartofaninexorabletrendtowardsthedigitalrepresentationofimagesIndeedthatmostpowerfulimageformofthetwentiethImageprocessingischaracterizedbyalargenumberofalgorithmsthatarespecificsolutionstospecificproblems．Somearemathematicalorcontext-independentoperationsthatareappliedtoeachandeverypixel．Forexample，wecanuseFouriertransformstoperformimagefilteringoperations．Othersare“algorithmic”—wemayuseacomplicatedrecursivestrategytofindthosepixelsthatconstitutetheedgesinanimage圖像處理不是一步就能完成的過程。可將它分成諸多步驟，必須一個接一個地執(zhí)行這些步驟，直到從被觀察的景物中提取出有用的數(shù)據(jù)。依據(jù)這種方法，一個層次化的處理方案，該圖給出了圖像處理不同階段的概觀。圖像處理首先是以適當(dāng)?shù)牡灰欢ㄊ枪鈱W(xué)的采集系統(tǒng)對圖像進行采集。在技術(shù)或科學(xué)應(yīng)用中，可以選擇一個適當(dāng)?shù)某上裣到y(tǒng)。此外，可以建立照明系統(tǒng)，選擇最佳波長范圍，以及選擇其他方案以便用最好的方法在圖像中獲取有用的對象特征。一旦圖像被檢測到，必須將其變成數(shù)字計算機可處理的形式，這個過程稱之為數(shù)字化。隨著交通問題的日益嚴重，智能交通系統(tǒng)應(yīng)運而生。汽車牌照自動識別系統(tǒng)是近幾年發(fā)展起來的計算機視覺和模式識別技術(shù)在智能交通領(lǐng)域應(yīng)用的重要研究課題之一。課題的目的是對攝像頭獲取的汽車圖像進行預(yù)處理,提出了車牌預(yù)處理、車牌緣檢測的頻率分析法。從經(jīng)過邊緣提取后的車輛圖像中提取車牌特征分析處理,從而初步定出車牌的區(qū)域，再利用車牌的先驗知識和分布特征對車牌區(qū)域二值化圖像進行處理，從而得到車牌的精確區(qū)域。在圖像中，對已知的干擾進行校正也是不可少的，比如由于光學(xué)聚焦不準，運動模糊，傳感器誤差以及圖像信號傳輸誤差所引起的干擾。在此還要涉及圖像重構(gòu)技術(shù)，它需要許多間接的成像技術(shù)，比如不直接提供圖像的射線斷層技術(shù)等。一套完整的處理步驟對于物體的分析和識別是必不可少的。首先，應(yīng)該采用適當(dāng)?shù)倪^濾技術(shù)以便從其他物體和背景中將所感興趣的物體區(qū)分出來。實質(zhì)上就是從一幅圖像（或者數(shù)幅圖像）中抽取出一幅或幾幅特征圖像。要完成這個任務(wù)最基本的工具就是圖像處理中所使用的求均值和邊緣檢測、單的相鄰像素分析，以及復(fù)雜的被稱為材質(zhì)描述的模式分析。物體的一個重要特性就是它的運動性。檢測和確定物體運動性的技術(shù)是必不可少的。隨后，該物體必須從背景中分離出來，這就意味著具有同樣特性和不同特性的區(qū)域必須被識別出來。這個過程產(chǎn)生出標志圖像。既然已經(jīng)知道了物體精確的幾何形狀，就可以抽取諸如平均灰度值、區(qū)域、邊界以及形成物體的其他參數(shù)等更多的信息。這些參數(shù)可用來對物體進行分類，這是許多圖像處理應(yīng)用中至關(guān)重要的一步，比如下面一些應(yīng)用：在一個顯示農(nóng)業(yè)地區(qū)的衛(wèi)星圖像中，想要區(qū)別出不同的果樹，并獲取參數(shù)以估算出成熟情況并監(jiān)測害蟲情況；在許多的醫(yī)學(xué)應(yīng)用中，最基本的問題是檢查病理變化，最典型的應(yīng)用就是染色體畸變分析；印刷體和手寫體識別是另一個例子，圖像處理一出現(xiàn)，人們就開始對它進行著研究，現(xiàn)在依然困難重重。人們希望能了解得更多一些，也就是試圖理解所讀到的內(nèi)容。這也是圖像處理的最后一個步驟，即理解所觀察到的景象。當(dāng)我們使用視覺系統(tǒng)時，實際上已或多或少無意識地在執(zhí)行這個任務(wù)。我們能識別不同的人，可以很輕易地區(qū)分出實驗室和起居室，可以觀察車流以便安全地穿行馬路。我們完成這樣的任務(wù)而并不了解視覺系統(tǒng)工作的奧秘。長久以來，圖像處理和計算機圖形學(xué)被看做兩個不同的領(lǐng)域。現(xiàn)在，人們在這兩個領(lǐng)域中的知識都有了極大的提高，并可以解決許多復(fù)雜的問題。計算機圖形學(xué)