數(shù)字圖像處理外文翻譯參考文獻(xiàn)

數(shù)字圖像處理外文翻譯參考文獻(xiàn)數(shù)字圖像處理外文翻譯參考文獻(xiàn)(文檔含中英文對照即英文原文和中文翻譯)原文：ApplicationOfDigitalImageProcessingInTheMeasurementOfCastingSurfaceRoughnessAhstract-Thispaperpresentsasurfaceimageacquisitionsystembasedondigitalimageprocessingtechnology.TheimageacquiredbyCCDispre-processedthroughtheprocedureofimageediting,imageequalization,theimagebinaryconversationandfeatureparametersextractiontoachievecastingsurfaceroughnessmeasurement.Thethree-dimensionalevaluationmethodistakentoobtaintheevaluationparametersandthecastingsurfaceroughnessbasedonfeatureparametersextraction.AnautomaticdetectioninterfaceofcastingsurfaceroughnessbasedonMATLABiscompiledwhichcanprovideasolidfoundationfortheonlineandfastdetectionofcastingsurfaceroughnessbasedonimageprocessingtechnology.Keywords-castingsurface;roughnessmeasurement;imageprocessing;featureparametersⅠ.INTRODUCTIONNowadaysthedemandforthequalityandsurfaceroughnessofmachiningishighlyincreased,andthemachinevisioninspectionbasedonimageprocessinghasbecomeoneofthehotspotofmeasuringtechnologyinmechanicalindustryduetotheiradvantagessuchasnon-contact,fastspeed,suitableprecision,strongabilityofanti-interference,etc[1,2].Asthereisnolawsaboutthecastingsurfaceandtherangeofroughnessiswide,detectionparametersjustrelatedtohighlydirectioncannotmeetthecurrentrequirementsofthedevelopmentofthephotoelectrictechnology,horizontalspacingorroughnessalsorequiresaquantitativerepresentation.Therefore,thethree-dimensionalevaluationsystemofthecastingsurfaceroughnessisestablishedasthegoal[3,4],surfaceroughnessmeasurementbasedonimageprocessingtechnologyispresented.Imagepreprocessingisdeducedthroughtheimageenhancementprocessing,theimagebinaryconversation.Thethree-dimensionalroughnessevaluationbasedonthefeatureparametersisperformed.AnautomaticdetectioninterfaceofcastingsurfaceroughnessbasedonMATLABiscompiledwhichprovidesasolidfoundationfortheonlineandfastdetectionofcastingsurfaceroughness.II.CASTINGSURFACEIMAGEACQUISITIONSYSTEMTheacquisitionsystemiscomposedofthesamplecarrier,microscope,CCDcamera,imageacquisitioncardandthecomputer.Samplecarrierisusedtoplacetestedcastings.Accordingtotheexperimentalrequirements,wecanselectafixedcarrierandthesamplelocationcanbemanuallytransformed,orselectcuringspecimensandthepositionofthesamplingstagecanbechanged.Figure1showsthewholeprocessingprocedure.,Firstly,thedetectedcastingsshouldbeplacedintheilluminatedbackgroundsasfaraspossible,andthenthroughregulatingopticallens,settingtheCCDcameraresolutionandexposuretime,thepicturescollectedbyCCDaresavedtocomputermemorythroughtheacquisitioncard.Theimagepreprocessingandfeaturevalueextractiononcastingsurfacebasedoncorrespondingsoftwarearefollowed.Finallythedetectingresultisoutput.III.CASTINGSURFACEIMAGEPROCESSINGCastingsurfaceimageprocessingincludesimageediting,equalizationprocessing,imageenhancementandtheimagebinaryconversation,etc.TheoriginalandclippedimagesofthemeasuredcastingisgiveninFigure2.Inwhicha)presentstheoriginalimageandb)showstheclippedimage.ImageEnhancementImageenhancementisakindofprocessingmethodwhichcanhighlightcertainimageinformationaccordingtosomespecificneedsandweakenorremovesomeunwantedinformationsatthesametime[5].Inordertoobtainmoreclearlycontourofthecastingsurfaceequalizationprocessingoftheimagenamelythecorrectionoftheimagehistogramshouldbepre-processedbeforeimagesegmentationprocessing.Figure3showstheoriginalgrayscaleimageandequalizationprocessingimageandtheirhistograms.Asshowninthefigure,eachgraylevelofthehistogramhassubstantiallythesamepixelpointandbecomesmoreflataftergrayequalizationprocessing.Theimageappearsmoreclearlyafterthecorrectionandthecontrastoftheimageisenhanced.Fig.2CastingsurfaceimageFig.3EqualizationprocessingimageB.ImageSegmentationImagesegmentationistheprocessofpixelclassificationinessence.Itisaveryimportanttechnologybythresholdclassification.Theoptimalthresholdisattainedthroughtheinstmctionthresh=graythresh(II).Figure4showstheimageofthebinaryconversation.ThegrayvalueoftheblackareasoftheImagedisplaystheportionofthecontourlessthanthethreshold(0.43137),whilethewhiteareashowsthegrayvaluegreaterthanthethreshold.Theshadowsandshadingemergeinthebrightregionmaybecausedbynoiseorsurfacedepression.Fig4BinaryconversationIV.ROUGHNESSPARAMETEREXTRACTIONInordertodetectthesurfaceroughness,itisnecessarytoextractfeatureparametersofroughness.Theaveragehistogramandvarianceareparametersusedtocharacterizethetexturesizeofsurfacecontour.Whileunitsurface'speakareaisparameterthatcanreflecttheroughnessofhorizontalworkpiece.Andkurtosisparametercanbothcharacterizetheroughnessofverticaldirectionandhorizontaldirection.Therefore,thispaperestablisheshistogramofthemeanandvariance,theunitsurface'speakareaandthesteepnessastheroughnessevaluatingparametersofthecastings3Dassessment.ImagepreprocessingandfeatureextractioninterfaceiscompiledbasedonMATLAB.Figure5showsthedetectioninterfaceofsurfaceroughness.Imagepreprocessingoftheclippedcastingcanbesuccessfullyachievedbythissoftware,whichincludesimagefiltering,imageenhancement,imagesegmentationandhistogramequalization,anditcanalsodisplaytheextractedevaluationparametersofsurfaceroughness.Fig.5AutomaticroughnessmeasurementinterfaceV.CONCLUSIONSThispaperinvestigatesthecastingsurfaceroughnessmeasuringmethodbasedondigitalImageprocessingtechnology.Themethodiscomposedofimageacquisition,imageenhancement,theimagebinaryconversationandtheextractionofcharacteristicparametersofroughnesscastingsurface.TheinterfaceofimagepreprocessingandtheextractionofroughnessevaluationparametersiscompiledbyMATLABwhichcanprovideasolidfoundationfortheonlineandfastdetectionofcastingsurfaceroughness.REFERENCE[1]XuDeyan,LinZunqi.Theopticalsurfaceroughnessresearchprogressanddirection[1].Opticalinstruments1996,18(1):32-37.[2]WangYujing.Turningsurfaceroughnessbasedonimagemeasurement[D].Harbin:HarbinUniversityofScienceandTechnology[3]BRADLEYC.Automatedsurfaceroughnessmeasurement[1].TheInternationalJournalofAdvancedManufacturingTechnology,2000,16(9):668-674.[4]LiChenggui,Lixing-shan,QiangXI-FU3Dsurfacetopographymeasurementmethod[J].Aerospacemeasurementtechnology,2000,20(4):2-10.[5]LiuHe.Digitalimageprocessingandapplication[M].ChinaElectricPowerPress,2005譯文：數(shù)字圖像處理在鑄件表面粗糙度測量中的應(yīng)用摘要—本文提出了一種表面圖像采集基于數(shù)字圖像處理技術(shù)的系統(tǒng)。由CCD獲得的圖像的步驟是通過預(yù)先處理圖像編輯，圖像均衡，圖像二進(jìn)制對話和特征參數(shù)的提取，實(shí)現(xiàn)鑄件表面粗糙度測量。三維評價方法是得到評價參數(shù)和鑄件表面粗糙度的特征參數(shù)的提取。一種基于MATLAB的鑄造表面粗糙度自動檢測接口程序，可以提供一個堅實(shí)的基礎(chǔ)在線和快速的基于圖像處理技術(shù)的鑄造表面粗糙度檢測。關(guān)鍵詞—鑄造表面粗糙度測量；圖像處理；特征參數(shù)Ⅰ.介紹如今在質(zhì)量和加工表面粗糙度的高度增加的需求下，由于如非接觸，熱點(diǎn)速度快，適用于精度高，抗干擾能力強(qiáng)等的優(yōu)點(diǎn)，基于圖像處理的機(jī)器視覺檢測已成為機(jī)械工業(yè)中主要測量技術(shù)之一[1,2]。由于沒有規(guī)定和限制，鑄件表面粗糙度的范圍是廣泛的，檢測參數(shù)與高度方向光電技術(shù)的發(fā)展，不能滿足目前的要求，水平間距或粗糙度也需要一個定量表示。因此，基于圖像處理技術(shù)的表面粗糙度測量方法，對鑄造表面粗糙度建立三維評價體系為目標(biāo)[3,4]。通過圖像增強(qiáng)處理，推導(dǎo)出圖像的預(yù)處理和圖像二值談話。三維粗糙度是基于特征參數(shù)進(jìn)行評價的。一種基于MATLAB的鑄造表面粗糙度自動檢測界面的編制提供了堅實(shí)的在線快速鑄造表面粗糙度檢測。Ⅱ.鑄件表面圖像采集系統(tǒng)采集系統(tǒng)由采樣載體，顯微鏡，CCD攝像頭，圖像采集卡和計算機(jī)組成。樣品載體是用來測試鑄件。根據(jù)實(shí)驗(yàn)要求，我們可以選擇一個固定的載體，采樣位置可以手動轉(zhuǎn)換，選擇固化試樣與采樣階段的位置是可以改變的。圖1顯示了整個加工過程，首先，檢測到鑄件應(yīng)盡可能放置在明亮的背景下，然后通過調(diào)節(jié)光學(xué)透鏡，設(shè)置CCD攝像機(jī)分辨率和曝光時間，對CCD采集到的圖片通過采集卡保存到計算機(jī)內(nèi)存。根據(jù)相應(yīng)的軟件對鑄件表面進(jìn)行圖像預(yù)處理和特征值提取，最后檢測結(jié)果輸出。圖1鑄造圖像采集系統(tǒng)Ⅲ.鑄件表面圖像處理鑄件表面圖像處理主要包括圖像編輯，均衡處理，圖像增強(qiáng)和圖像二值談話等。原始的圖像測量鑄件圖2中給出。其中（a）顯示了原始圖像和（b)顯示剪輯圖像。圖像增強(qiáng)圖像增強(qiáng)是一種處理方法，可以突出某些圖像信息,根據(jù)特定的需要同時可以削弱或刪除一些不必要的信息[5]。為了獲得更清楚輪廓的鑄件表面均勻化處理的圖像即校正圖像的直方圖應(yīng)在圖像分割處理前預(yù)先處理。圖3顯示了原始灰度圖像及其直方圖均衡化處理的圖像。如圖所示，每個灰度級的直方圖具有基本相同的像素點(diǎn)，灰度均衡化處理后變得更加平。校正后的對比度增強(qiáng)的圖像將變得更加清晰。a)原始圖像b)修剪圖像圖2鑄件表面圖像a)灰度圖像b)直方圖c)均衡圖像d）均衡直方圖圖3均衡處理圖像B.圖像分割圖像分割是在本質(zhì)上的像素分類的過程。它是由閾值分類的一個非常重要的技術(shù)。最優(yōu)閾值是通過instmction脫粒=graythresh（II）達(dá)到的。圖4顯示圖像的二進(jìn)制談話。圖中的黑色區(qū)域顯示部分的輪廓的灰度值低于閾值（0.43137），而白色區(qū)域表示灰度值大于閾值。陰影和陰影在明亮的區(qū)域出現(xiàn)可能造成噪音或表面凹陷。a)灰度圖像b)二值圖像圖4圖像的二值化Ⅳ.粗糙度參數(shù)提取為了檢測表面粗糙度，需要提取粗糙度特征參數(shù)。平均直方圖和方差是用來描述表面輪廓紋理尺寸參數(shù)。而單位表面的峰面積參數(shù)能反映工件的粗糙度水平。峰度參數(shù)可以表征