纹理物体缺陷的视觉检测算法研究优秀毕业论文

工学硕士学位论文纹理物体缺陷的视觉检测算法研究DEVELOPMENTOFAVISUALINSPECTIONALGORITHMTODETECTDEFECTFORTEXTURALOBJECT吴晶晶哈尔滨工业大学2008年12月国内图书分类号TP7511国际图书分类号620工学硕士学位论文纹理物体缺陷的视觉检测算法研究硕士研究生吴晶晶导师李泽湘教授副导师邓江汶博士申请学位工学硕士学科、专业控制科学与工程所在单位深圳研究生院答辩日期2008年12月授予学位单位哈尔滨工业大学CLASSIFIEDINDEXTP7511UDC620DISSERTATIONFORTHEMASTERDEGREEOFENGINEERINGDEVELOPMENTOFAVISUALINSPECTIONALGORITHMTODETECTDEFECTFORTEXTURALOBJECTCANDIDATEJINGJINGWUSUPERVISORCOSUPERVISORPROFZEXIANGLIDRJIANGWENDENGACADEMICDEGREEAPPLIEDFORMASTEROFENGINEERINGSPECIALTYCONTROLSCIENCEANDENGINEERINGAFFILIATIONSHENZHENGRADUATESCHOOLDATEOFDEFENCEDECEMBER,2008DEGREECONFERRINGINSTITUTIONHARBININSTITUTEOFTECHNOLOGYI摘要在竞争激烈的工业自动化生产过程中，机器视觉对产品质量的把关起着举足轻重的作用，机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检测技术相比，自动化的视觉检测系统更加经济、快捷、高效与安全。纹理物体在工业生产中广泛存在，像用于半导体装配和封装底板和发光二极管，现代化电子系统中的印制电路板，以及纺织行业中的布匹和织物等都可认为是含有纹理特征的物体。本论文主要致力于纹理物体的缺陷检测技术研究，为纹理物体的自动化检测提供高效而可靠的检测算法。纹理是描述图像内容的重要特征，纹理分析也已经被成功的应用与纹理分割和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检测算法。这种算法能容忍物体变形引起的图像配准误差，对纹理的影响也具有鲁棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义，如缺陷区域的大小、形状、亮度对比度及空间分布等。同时，在参考图像可行的情况下，本算法可用于同质纹理物体和非同质纹理物体的检测，对非纹理物体的检测也可取得不错的效果。在整个检测过程中，我们采用了可调控金字塔的纹理分析和重构技术。与传统的小波纹理分析技术不同，我们在小波域中加入处理物体变形和纹理影响的容忍度控制算法，来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段，我们检测了一系列具有实际应用价值的图像。实验结果表明本文提出的纹理物体缺陷检测算法具有高效性和易于实现性。关键字缺陷检测；纹理；物体变形；可调控金字塔；重构IIABSTRACTMACHINEVISIONPLAYSANIMPORTANTROLEINQUALITYCONTROLTASKSFORTODAYSHIGHLYCOMPETITIVEAUTOMATICMANUFACTURINGINTERMSOFDEFECTDETECTIONAPPLICATIONS,ANAUTOMATEDVISUALINSPECTIONSYSTEMISSUPERIORTOCONVENTIONALINSPECTIONTECHNIQUESINMANYASPECTS,SUCHASECONOMY,CONVENIENCE,HIGHEFFICIENCYANDSAFETYETCINTHISTHESIS,WEMAINLYCONCERNABOUTTHEDEFECTDETECTIONPROBLEMSFORTEXTURALOBJECTS,WHICHCANBEFOUNDINMANYINDUSTRIALINSPECTIONAPPLICATIONSLIKESUBSTRATEBOARDSANDLIGHTEMITTINGDIODESLEDSFORSEMICONDUCTORASSEMBLYANDPACKAGINGPROCESSES,PRINTEDCIRCUITBOARDSPCBSANDTEXTUREDMATERIALSASANIMPORTANTCHARACTERISTICFORTHEANALYSISOFIMAGES,TEXTUREHASBEENSUCCESSFULLYUTILIZEDFORAVARIETYOFMACHINEVISIONAPPLICATIONS,SUCHASTEXTURESEGMENTATION,TEXTURECLASSIFICATIONANDETCINTHISSTUDY,WEPRESENTANEWIMAGEREFERENCEAPPROACHFORINDUSTRIALINSPECTIONAPPLICATIONSTHEPROPOSEDMETHODDOESNOTREQUIREACCURATEIMAGEREGISTRATIONANDISROBUSTTOTHEINFLUENCEOFTEXTURESUNLIKEOTHERTEXTUREANALYSISTECHNIQUES,THEPROPOSEDMETHODPROVIDESUSEFULPHYSICALINFORMATIONFORTHECHARACTERISTICSOFDEFECTSDURINGTHEINSPECTIONPROCESS,WEADOPTANEFFICIENTWAVELETTEXTUREANALYSISTECHNIQUEOFSTEERABLEPYRAMIDANDITSRECONSTRUCTIONSCHEMEATOLERANCECONTROLALGORITHMFORHANDLINGOBJECTDISTORTIONANDTEXTURESINWAVELETDOMAINISINTRODUCEDAFTERCOMBINATIONOFTHEDIFFERENCESUBIMAGESINEACHDECOMPOSITIONLEVELFORBACKWARDSTEERABLEPYRAMID,DETECTIONRESULTSAREFINALLYOBTAINEDEXPERIMENTALRESULTSFROMASSEMBLEDSUBSTRATEBOARDSFORSEMICONDUCTORPACKAGE,PCB,LEDANDTEXTUREDMATERIALSAREAVAILABLEHAVEDEMONSTRATEDTHATTHEPROPOSEDMETHODISEASYTOIMPLEMENTANDVERYEFFECTIVEKEYWORDSDEFECTDETECTION,TEXTURE,OBJECTDISTORTION,STEERABLEPYRAMID,RECONSTRUCTIONIIIACKNOWLEDGEMENTSIWISHTOACKNOWLEDGEASMASSEMBLYAUTOMATIONLTDFORSPONSORINGTHEWHOLEWORKTHEN,IWOULDLIKETOEXPRESSMYSINCEREGRATITUDETOMYADVISORS,PROFZEXIANGLIANDDRJIANGWENDENG,FORTHEIRNUMEROUSENCOURAGEMENTS,GUIDANCE,ANDMANYVALUABLEDISCUSSIONSDURINGTHEWRITINGOFTHISDISSERTATIONANDTHERESEARCHIAMESPECIALLYGRATEFULTOMYFRIENDSHAIXIAMENGANDJUNZHANGFORBEINGAGREATHELPINGHANDTHROUGHOUTTHEPROJECTFINALLY,IWOULDLIKETOTHANKMYFAMILYANDTHEFRIENDSWHOCAREANDSUPPORTMEALLTHETIMEIVCONTENTSPAGELISTOFTABLESVILISTOFFIGURESVII1INTRODUCTION111BACKGROUND112MOTIVATION513RESEARCHCONTENT614THESISORGANIZATION62TEXTUREANALYSISTECHNIQUES721TEXTUREANALYSIS722STATISTICALMETHOD923MODELBASEDMETHOD1024SPECTRALMETHOD1125DEFECTDETECTIONUSINGTEXTUREANALYSISTECHNIQUES133WAVELETTRANSFORMFORINSPECTION1531TWODIMENSIONALWAVELETTRANSFORM1532WAVELETIMAGEDECOMPOSITIONANDRECONSTRUCTION1733DEFECTDETECTIONUSINGWAVELETTRANSFORM1834IMAGESUBTRACTIONUSINGWAVELETTRANSFORM204DEFECTDETECTIONFORTEXTURALOBJECT2241ALGORITHMDESCRIPTION2242THESTEERABLEPYRAMID2443TOLERANCECONTROLALGORITHM29V44EXPERIMENTALRESULTSANDDISCUSSION325GRAPHICUSERINTERFACEDESIGN4151INTRODUCTIONTOGUI4152GUIDESIGNFORTEXTURALOBJECTINSPECTIONSYSTEM4353GUIIMPLEMENTATIONFORTEXTURALOBJECTINSPECTIONSYSTEM46CONCLUSION52REFERENCES53VILISTOFTABLESTABLEPAGETABLE41PROPERTIESOFTHESTEERABLEPYRAMIDCOMPAREDTODYADICWAVELET29TABLE42THEDETAILEDDESCRIPTIONOFTOLERANCECONTROLALGORITHM31VIILISTOFFIGURESFIGUREPAGEFIGURE11AUTOMATEDVISUALINSPECTIONSYSTEM2FIGURE21THEINTERPRETATIONOFTEXTURES8FIGURE22BASICFLOWFORDEFECTDETECTIONBYUSINGTEXTUREANALYSISTECHNIQUES13FIGURE31SYSTEMDIAGRAMOFWAVELETTRANSFORM17FIGURE32BASICFLOWOFTEXTUREDEFECTDETECTIONUSINGWAVELETTRANSFORM19FIGURE41OVERALLBLOCKDIAGRAMOFTHEPROPOSEDALGORITHM23FIGURE42REFERENCEINFORMATIONEXTRACTEDFROMTHEREFERENCEIMAGE24FIGURE43SYSTEMDIAGRAMOFSTEERABLEPYRAMID25FIGURE44STRUCTUREOFSTEERABLEPYRAMID26FIGURE45SPECTRALDECOMPOSITIONOFSTEERABLEPYRAMID27FIGURE46THEEFFECTOFSPACEPATTERNINSPECTION32FIGURE47THEEFFECTOFMINUTEDEFECTINSPECTION34FIGURE48THEEFFECTOFDECOMPOSITIONLEVELSFORP1435FIGURE49THEEFFECTOFDECOMPOSITIONLEVELSFORP3636FIGURE410LEDINSPECTION137FIGURE411LEDINSPECTION238FIGURE412PCBINSPECTION138FIGURE413PCBINSPECTION239FIGURE414TEXTUREINSPECTION140FIGURE415TEXTUREINSPECTION240FIGURE51THEPROCESSOFGUIDESIGN43FIGURE52MAINFLOWOFGUIDESIGNFORTEXTURALOBJECTINSPECTIONSYSTEM44VIIIFIGURE53MENUEDITOR45FIGURE54GUICOMPONENTSASSIGNMENTEDITING45FIGURE55THEMAININTERFACEOFGUI47FIGURE56LOADINPUTIMAGESANDIMAGENORMALIZATION48FIGURE57DEFECTDETECTIONRESULTSDISPLAY48FIGURE58THEINTERFACEFORIMAGECOMPARISONDETAILSDISPLAY49FIGURE59HIGHPASSBANDCOEFFICIENTIMAGES49FIGURE5103SCALEAND4ORIENTATIONLOWPASSBANDCOEFFICIENTIMAGES50FIGURE5112SCALEBANDPASSCOEFFICIENTIMAGESINORIENTATIONOFZERO50FIGURE5122SCALEBANDPASSCOEFFICIENTIMAGESINORIENTATIONOF/4P51CHAPTER1INTRODUCTION1CHAPTER1INTRODUCTION11BACKGROUNDQUALITYCONTROLISDESIGNEDTOENSURETHATDEFECTIVEPRODUCTSARENOTALLOWEDTOREACHTHECUSTOMERFORTHISREASON,QUALITYCONTROLACTIVITIESFORMANESSENTIALINFORMATIONFEEDBACKLOOPFORTHEWHOLEBUSINESS,WITHPOTENTIALINFLUENCEONTHEDESIGN,PROCESSPLANNINGANDLOGISTICSFUNCTIONSASWELLASONMANUFACTURING1THEGLOBALECONOMICPRESSURESHAVEGRADUALLYLEDBUSINESSESTOASKMOREOFTECHNICALINNOVATIONINORDERTOBECOMEMORECOMPETITIVEANDDOBETTERINQUALITYCONTROLOBVIOUSLY,MANUALINSPECTORSLOWSDOWNTHEENTIREQUALITYCONTROLPROCESSASITBECOMESCOSTLY,TIMECONSUMINGANDALSOMAYIMPACTTHEEFFECTIVENESSOFHUMANLABORDUETOTHEHAZARDOUSATMOSPHEREOFINDUSTRYHOWEVER,WITHTHEDEVELOPMENTOFCOMPUTERSCIENCEANDTECHNOLOGY,THEAPPLICATIONOFMACHINEVISIONSYSTEMSFORTHEAUTOMATIONOFQUALITYCONTROLTASKSHASBECOMEQUITEPOPULARINTHEASPECTOFDEFECTDETECTIONAPPLICATIONS,ANAUTOMATEDVISUALINSPECTIONSYSTEMISEXPECTEDTOPROVIDEASOLUTIONWITHRELIABLEANDEFFECTIVEPERFORMANCEAUTOMATEDVISUALINSPECTIONTHETERMINSPECTIONIMPLIESAPROCEDUREOFDETERMININGWHETHERTHEPRODUCTHASDEVIATEDFROMAGIVENSETOFSPECIFICATIONSVISUALINSPECTIONISCOMMONLYDEFINEDAS“THEEXAMINATIONOFAMATERIAL,COMPONENT,ORPRODUCTFORCONDITIONSOFNONCONFORMANCEUSINGLIGHTANDTHEEYES,ALONEORINCONJUNCTIONWITHVARIOUSAIDS”VISUALINSPECTIONSYSTEMINTHISTHESISISRELATEDBYUSINGVISIBLELIGHT,ALTHOUGHITCANCHAPTER1INTRODUCTION2BECARRIEDOUTBYUSINGOTHERFORMSOFRADIATIONLIKEULTRASOUND,ULTRAVIOLET,INFRAREDANDXRAYVISUALINSPECTIONSYSTEMCANBEDESCRIBEDASTHEINTEGRATIONOFIMAGEACQUISITIONDEVICES,COMPUTERS,ANDIMAGINGSOFTWARETHEAUTOMATEDMANUFACTURINGALSOREQUIRESAPPLICATIONSPECIFICMATERIALHANDLING,MOTIONCONTROLLERS,PARTTRACKINGCONTROLLERSANDSENSORS,ANDCONTROLSOFTWARE2FIGURE11SHOWSANEXAMPLEOFAUTOMATEDVISUALINSPECTIONSYSTEM2FIGURE11AUTOMATEDVISUALINSPECTIONSYSTEMIMAGEACQUISITIONDEVICESLIGHTINGTOILLUMINATETHEOBJECT,OPTICS/LENSTOCOUPLETHEIMAGETOACAMERASENSOR,ACAMERATOCONVERTOPTICALIMAGETOANANALOGSIGNAL,ANDFORAUTOMATEDMANUFACTURINGATRIGGERSENSORTOINITIATETHEIMAGEACQUISITIONCOMPUTERTYPICALLYAPCWITHACPUDECISIONMAKINGANDCONTROL,ANOPERATORCONSOLETHEOPERATORINTERFACE,ANDIMAGINGHARDWARETYPICALLYANANALOGTODIGITALCONVERTERANDIMAGEPROCESSINGTHEAUTOMATEDMANUFACTURINGALSOREQUIRESANADDITIONOFI/OINTERFACE,MATERIALHANDLINGANDCOMMUNICATIONSTOCUSTOMERPROCESSINFORMATIONANDCONTROLSYSTEMSOFTWAREMODEMNETWORKOPERATORCONSOLEI/OINTERFACECOMPUTERIMAGINGELECTRONICSIMAGINGELECTRONICSCAMERACAMERALIGHTINGLIGHTINGLENSLENSMATERIALHANDLINGCHAPTER1INTRODUCTION3SOFTWAREOPERATINGSYSTEMSOFTWAREEGWINDOWSNT/2000/XPANDIMAGINGSOFTWARETHEUSERINTERFACE,IMAGEACQUISITIONANDPROCESSING,IMAGEANALYSISANDDECISIONMAKING,HISTORICALDATACOLLECTIONANDANALYSISTODAYSHIGHSPEED,COMPLEXMANUFACTURINGSYSTEMSREQUIREAVISUALINSPECTIONSYSTEMTOHAVETWOOBJECTIVESONEISQUALITYCONTROLTHEOTHERISFORMONITORINGTHEPRODUCTIONPROCESSWITHTHEADVANCEMENTOFTECHNICALINNOVATION,MOREANDMOREBENEFITSBECAMEAPPARENTTHEAUTOMATEDVISUALINSPECTIONSYSTEMCANBEDESIGNEDTOHAVETHEFOLLOWINGADVANTAGES1LOWERCOST2HIGHERSPEED3QUALITYASSURANCE4ACCURACYANDRELIABILITY5IMPROVEDSAFETYHOWEVER,AUTOMATEDVISUALINSPECTIONSYSTEMSALSOHAVELIMITATIONS3FIRSTLY,THEDESIGNANDINSTALLATIONISAVERYCOMPLEXPROBLEMANDREQUIRESTECHNICALEXPERTISEINTHEAREASOFILLUMINATION,CAMERAS,NETWORKING,PATTERNRECOGNITIONANDPROGRAMMINGANOTHERISTHECURRENTLYAVAILABLEAUTOMATEDVISUALINSPECTIONSYSTEMSARECUSTOMERORIENTEDFORASPECIFICTASKANDLACKOFFLEXIBILITYSOMETIMESOTHERKINDSOFPROBLEMS,SUCHASHIGHINITIALCOSTOFTHESYSTEM,NEEDTOBECONSIDEREDINSPECTIONOFTEXTURALOBJECTTEXTUREISAPHENOMENONTHATISWIDESPREAD,EASYTORECOGNIZEANDHARDTODEFINEHUMANSOFTENDESCRIBETEXTUREASFINE,COARSE,GRAINED,ANDSMOOTH,BUTTHESEDESCRIPTIONSAREQUITEIMPRECISEANDNONQUANTITATIVEINSPITEOFNOPRECISEDEFINITION,ONETHINGABOUTTEXTUREISGENERALLYASSUMEDTOBETRUETEXTUREISABOUTAREGION,ANDTEXTUREOFAPOINTISUNDEFINEDMOREFORMALLY,TEXTURECANBEDESCRIBEDASTHESETOFLOCALNEIGHBORHOODPROPERTIESOFANIMAGEREGIONTEXTUREEXISTSINVARIOUSINDUSTRIALINSPECTIONAPPLICATIONSLIKESUBSTRATEBOARDSANDLIGHTEMITTINGDIODESLEDSFORSEMICONDUCTORASSEMBLYANDPACKAGINGPROCESSES,PRINTEDCIRCUITBOARDSPCBSANDCHAPTER1INTRODUCTION4TEXTUREDMATERIALSINTHISTHESIS,WEDEFINEOBJECTSTHATCONTAINTEXTUREPROPERTIESASTEXTURALOBJECTWITHTHISDEFINITION,ASETOFINDUSTRIALINSPECTIONPROBLEMSCANBECONSIDEREDASASINGLEONEPROBLEM,WHICHENHANCESTHEINSPECTIONCAPABILITIESINGENERAL,SURFACEINSPECTIONFALLSINTOONEOFTHETHREECATEGORIESREFERENCECOMPARISON,NONREFERENTIALAPPROACHESANDHYBRIDAPPROACHESCOMBINATIONOFTHEABOVETWOMETHODS4REFERENCECOMPARISONMETHODISTHESIMPLESTMETHOD,INCLUDINGIMAGESUBTRACTION,FEATUREMATCHINGANDPHASEONLYMETHODIMAGESUBTRACTIONPROVIDESTHEMOSTUSEFULPHYSICALMEANINGSINCHARACTERIZATIONOFDEFECTPATTERNS,BUTONEOFTHEBIGGESTPRACTICALPROBLEMSISIMAGEREGISTRATION5INFEATUREMATCHING,THEEXTRACTEDFEATURESFROMTHEOBJECTANDTHOSEDEFINEDBYTHEMODELARECOMPAREDBUTITISLIMITEDBYUSINGENORMOUSNUMBEROFTEMPLATESANDLACKSOFSTABILITYPHASEONLYMETHODUSESFOURIERTRANSFORMBYNORMALIZATIONOFTHEIMAGEAMPLITUDESOITCANTOLERATEROTATIONSOFTHEIMAGESTOBECOMPAREDINSOMEEXTENTPRACTICALPROBLEMSOFOBJECTDISTORTIONCANNOTTOTALLYBERESOLVEDNONREFERENTIALAPPROACHESAREWORKINGONTHEIDEATHATAPATTERNISDEFECTIVEIFITDOESNOTCONFIRMTOTHEDESIGNSPECIFICATIONSTANDARDS,PREDEFINEDMODELSORDESIGNRULESOFSPECIFICPATTERN6,7BUTITONLYWORKSWELLINIDENTIFYINGSOMEKINDSOFDEFECTSHYBRIDAPPROACHESCOMBINESTHEREFERENCECOMPARISONANDNONREFERENCEMETHODSALONE,ANDCOVERSALARGEVARIETYOFDEFECTS8,9HOWEVER,ITISMUCHCOMPLICATEDTODESIGNANEFFICIENTHYBRIDDETECTORTEXTURE,ASANIMPORTANTCUEFORTHEANALYSISOFIMAGES,HASBEENSUCCESSFULLYUTILIZEDINAUTOMATEDINSPECTION1014THEAVAILABLEMETHODSMIGHTBECATEGORIZEDINTOSTATISTICAL,MODELBASEDANDSPECTRALMETHODS15SPATIALGRAYLEVELCOOCCURRENCEMATRICESGLCM16AREONEOFTHEMOSTWELLKNOWNANDWIDELYUSEDTEXTUREFEATURESIIVARINENETALAPPLIEDCOOCCURRENCETEXTUREFEATURESTODETECTINGDEFECTSINPAPERWEBWHERETHENORMALTEXTURESHAVECHARACTERISTICFREQUENCY17OJALAETALFIRSTINTRODUCEDLOCALBINARYPATTERNLBPOPERATOR18,WHICHHASEVOLVEDTOPRESENTAMAJORBREAKTHROUGHINTEXTUREANALYSISITHASBEENAPPLIEDTODEFECTDETECTIONONCERAMICTILECHAPTER1INTRODUCTION5SURFACES19,ANDWOOD20INTHELASTSEVERALDECADES,THEMULTICHANNELGABORFILTERS21,22AREWELLRECOGNIZEDASAJOINTSPATIAL/SPATIALFREQUENCYREPRESENTATIONFORANALYZINGTEXTUREDIMAGESWITHHIGHLYSPECIFICFREQUENCYANDORIENTATIONCHARACTERISTICSVANDEWOUWERETAL23EXTENDEDTHEMULTIRESOLUTIONWAVELETTECHNIQUESTOCOLORTEXTURECLASSIFICATIONDUMINGTSAI24PROPOSEDAMULTIRESOLUTIONAPPROACHFORTEXTUREINSPECTIONBASEDONANEFFICIENTIMAGERESTORTATIONSCHEMEUSINGWAVELETTRANSFORM12MOTIVATIONFROMTHEDESCRIPTIONINSEC11,ITCANBECONCLUDEDTHATVISUALINSPECTIONSYSTEMISATRENDOFTECHNICALINNOVATIONFORAUTOMATEDMANUFACTURINGDEFECTDETECTIONALGORITHMISTHECOREPARTOFANYVISUALINSPECTIONSYSTEMANDISRESPONSIBLEFORITSCOMMERCIALSUCCESSDEFECTDETECTIONALGORITHMSAREEXPECTEDTOPROVIDEASOLUTIONWITHRELIABLEANDEFFECTIVEPERFORMANCEIMAGESUBTRACTION,ASTHESIMPLESTANDMOSTDIRECTREFERENTIALAPPROACH,ISQUITEUSEFULUNDERTHECONDITIONOFPRECISEIMAGEREGISTRATIONITISSUPPOSEDTOVERIFYALLTHEPOTENTIALFLAWSWITHOUTLOSINGANYPHYSICALMEANINGS,SUCHASTHESIZE,SHAPE,INTENSITYCONTRASTANDSPATIALARRANGEMENTUNFORTUNATELY,DUETOOBJECTDISTORTIONORTHEINFLUENCEOFTEXTURES,IMAGEREGISTRATIONINACCURACYAPPEARSANDBRINGSFALSEDEFECTSHOWEVER,ACCORDINGTOTHEFACTTHATTHEFREQUENCYINFORMATIONANDLOCATIONOFMISALIGNMENTCHANGESCAUSEDBYOBJECTDISTORTIONORTEXTURESOFTENEXISTINACERTAINAREA,IFWECANFINDAPROPERWAYTOACCURATELYDETERMINEATOLERANCECONTROLZONEFOREACHPIXELINTHEREFERENCEANDINSPECTEDIMAGES,ITWILLENABLEUSTOREDUCETHEMISJUDGMENTONTHISDEFECTDETECTIONALGORITHMSAREEXPECTEDTOBEFLEXIBLEMOSTOFTHEINSPECTIONALGORITHMSAREDESIGNEDFORASPECIFICTASKANDLACKOFFLEXIBILITYFORTEXTUREDMATERIALS,MOSTOFTHEPREVIOUSTEXTUREINSPECTIONTECHNIQUESONLYFOCUSONTHEINSPECTIONOFLOCALDEFECTSEMBEDDEDINHOMOGENEOUSTEXTUREDSURFACESALSOSOMECOMPLICATEDALGORITHMSAREDIFFICULTTOPROCESSBYONLYSOFTWAREADDITIONALPARALLELPIPELINEARCHITECTURESAREREQUIREDFORHIGHSPEEDPROCESSINGITISNECESSARYTODEVELOPVISUALCHAPTER1INTRODUCTION6INSPECTIONALGORITHMSWHICHCANENHANCETHEINSPECTIONCAPABILITIESOFPRESENTSYSTEMSANDALSOHAVEAVARIETYOFINDUSTRIALINSPECTIONAPPLICATIONS13RESEARCHCONTENT1RESEARCHONTEXTUREANALYSISTECHNIQUES2RESEARCHONWAVELETTEXTUREANALYSISMETHODS,ANDUSINGSTEERABLEPYRAMIDDECOMPOSITIONANDRECONSTRUCTIONFORDEFECTDETECTION3DEVELOPINGTOLERANCECONTROLALGORITHMSTODEALWITHOBJECTDISTORTIONANDTHEINFLUENCEOFTEXTURES4ALGORITHMIMPROVEMENTSFORDETAILSCONSIDERINGLIKEMINUTEDEFECTSONNARROWLINES,PATTERNEDGES,SPACEPATTERNSANDILLUMINATIONETC5SUMMARIZINGTHEEXPERIMENTSANDTHEORETICALCONCLUSION6USERCONSOLEGRAPHICALUSERINTERFACEDESIGN14THESISORGANIZATIONTHEREMAININGSECTIONSOFTHISTHESISAREORGANIZEDASFOLLOWSCHAPTER2,WESURVEYTHERELATEDWORKSTOTEXTUREANALYSISTECHNIQUESWAVELETTEXTUREANALYSISTECHNIQUESFORDEFECTDETECTIONSAREDESCRIBEDINCHAPTER3CHAPTER4EXPLAINSTHESTEERABLEPYRAMIDANDDEFECTDETECTIONALGORITHMEXPERIMENTSRESULTSAREALSOILLUSTRATEDANDDISCUSSEDINCHAPTER5,GRAPHICUSERINTERFACEISPROPOSEDCONCLUSIONSAREGIVENINTHEFINALSECTIONCHAPTER2TEXTUREANALYSISTECHNIQUES7CHAPTER2TEXTUREANALYSISTECHNIQUESASANIMPORTANTCUEFORTHEANALYSISOFIMAGES,TEXTUREHASBEENSUCCESSFULLYUTILIZEDINAUTOMATEDINSPECTIONMOSTOFTHEPREVIOUSTEXTUREINSPECTIONTECHNIQUESFOCUSONTHEUNIFORMTEXTUREDMATERIALSSUCHASTEXTILEFABRICSOMEWORKSONRANDOMTEXTUREDMATERIALSAREALSOINCLUDEDMANYTEXTUREANALYSISMETHODSHAVEBEENPROPOSEDINTHEPASTSEVERALDECADESTHEAVAILABLEMETHODSMIGHTBECATEGORIZEDINTOSTATISTICAL,MODELBASEDANDSPECTRALMETHODSUSEFULPHYSICALMEANINGINCHARACTERIZATIONOFDEFECTPATTERNSISANIMPO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