数字图像处理外文翻译参考文献

数字图像处理外文翻译参考文献数字图像处理外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)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译文：数字图像处理在铸件表面粗糙度测量中的应用摘要—本文提出了一种表面图像采集基于数字图像处理技术的系统。由CCD获得的图像的步骤是通过预先处理图像编辑，图像均衡，图像二进制对话和特征参数的提取，实现铸件表面粗糙度测量。三维评价方法是得到评价参数和铸件表面粗糙度的特征参数的提取。一种基于MATLAB的铸造表面粗糙度自动检测接口程序，可以提供一个坚实的基础在线和快速的基于图像处理技术的铸造表面粗糙度检测。关键词—铸造表面粗糙度测量；图像处理；特征参数Ⅰ.介绍如今在质量和加工表面粗糙度的高度增加的需求下，由于如非接触，热点速度快，适用于精度高，抗干扰能力强等的优点，基于图像处理的机器视觉检测已成为机械工业中主要测量技术之一[1,2]。由于没有规定和限制，铸件表面粗糙度的范围是广泛的，检测参数与高度方向光电技术的发展，不能满足目前的要求，水平间距或粗糙度也需要一个定量表示。因此，基于图像处理技术的表面粗糙度测量方法，对铸造表面粗糙度建立三维评价体系为目标[3,4]。通过图像增强处理，推导出图像的预处理和图像二值谈话。三维粗糙度是基于特征参数进行评价的。一种基于MATLAB的铸造表面粗糙度自动检测界面的编制提供了坚实的在线快速铸造表面粗糙度检测。Ⅱ.铸件表面图像采集系统采集系统由采样载体，显微镜，CCD摄像头，图像采集卡和计算机组成。样品载体是用来测试铸件。根据实验要求，我们可以选择一个固定的载体，采样位置可以手动转换，选择固化试样与采样阶段的位置是可以改变的。图1显示了整个加工过程，首先，检测到铸件应尽可能放置在明亮的背景下，然后通过调节光学透镜，设置CCD摄像机分辨率和曝光时间，对CCD采集到的图片通过采集卡保存到计算机内存。根据相应的软件对铸件表面进行图像预处理和特征值提取，最后检测结果输出。图1铸造图像采集系统Ⅲ.铸件表面图像处理铸件表面图像处理主要包括图像编辑，均衡处理，图像增强和图像二值谈话等。原始的图像测量铸件图2中给出。其中（a）显示了原始图像和（b)显示剪辑图像。图像增强图像增强是一种处理方法，可以突出某些图像信息,根据特定的需要同时可以削弱或删除一些不必要的信息[5]。为了获得更清楚轮廓的铸件表面均匀化处理的图像即校正图像的直方图应在图像分割处理前预先处理。图3显示了原始灰度图像及其直方图均衡化处理的图像。如图所示，每个灰度级的直方图具有基本相同的像素点，灰度均衡化处理后变得更加平。校正后的对比度增强的图像将变得更加清晰。a)原始图像b)修剪图像图2铸件表面图像a)灰度图像b)直方图c)均衡图像d）均衡直方图图3均衡处理图像B.图像分割图像分割是在本质上的像素分类的过程。它是由阈值分类的一个非常重要的技术。最优阈值是通过instmction脱粒=graythresh（II）达到的。图4显示图像的二进制谈话。图中的黑色区域显示部分的轮廓的灰度值低于阈值（0.43137），而白色区域表示灰度值大于阈值。阴影和阴影在明亮的区域出现可能造成噪音或表面凹陷。a)灰度图像b)二值图像图4图像的二值化Ⅳ.粗糙度参数提取为了检测表面粗糙度，需要提取粗糙度特征参数。平均直方图和方差是用来描述表面轮廓纹理尺寸参数。而单位表面的峰面积参数能反映工件的粗糙度水平。峰度参数可以表征