原创版图像增强外文文献及翻译Word文档格式.doc

原创版图像增强外文文献及翻译Word文档格式.doc

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@#@外文文献@#@AnEffectiveAutomaticImageEnhancementMethod@#@ABSTRACTOtsumethodispropertodealwithtwoconditions:

@#@

（1）twoormoreclasseswithdistintivegray-valuesrespectively;@#@

（2）classeswithoutdistinctivegray-values,butwithsimilarareas.However,whenthegray-valuedifferencesamongclassesarenotsodistinct,andtheobjectissmallrelativetobackgroud,theseparabilitiesamongclassesareinsufficient.Inordertoovercometheaboveproblem,thispaperpresentsanimprovedspatiallow-passfilterwithaparameterandpresentsanunsupervisedmethodofautomaticparameterselectionforimageenhancementbasedonOtsumethod.Thismethodcombinesimageenhancementwithimagesegmentationasoneprocedurethroughadiscriminantcriterion.Theoptimalparameterofthefilterisselectedbythediscriminantcriteriongiventomaximizetheseparabilitybetweenobjectandbackground.Theoptimalthresholdforimagesegmentationiscomputedsimultaneously.Themethodisusedtodetectthesurfacedefectofcontainer.Experimentsillustratethevalidityofthemethod.@#@KEYWORDSimageprocessing;@#@automatedimageenhancement;@#@imagesegmentation;@#@automatedvisualinspection@#@1Introduction@#@Automatedvisualinspectionofcrackedcontainer（AVICC）isapracticalapplicationofmachinevisiontechnology.Torealizeourgoal,fouressentialoperationsmustbedealtwith–imagepreprocessing,objectdetection,featuredescriptionandfinalcrackedobjectclassification.Imageenhancementistoprovidearesultmoresuitablethanoriginalimageforspecificapplications.Inthispapertheobjectiveofenhancement,followedbyimagesegmentation,istoobtainanimagewithahighercontentabouttheobjectinterestingwithlesscontentaboutnoiseandbackground.Gonzalez[1]discussesthatimageenhancementapproachesfallintotwomaincategories,inthatspatialdomainandfrequencydomainmethods.Burton[2]appliesimageaveragingtechniquetofacerecognitionsystem,makingitabletorecognisefamiliarfaceseasilyacrosslargevariationsinimagequality.Centeno[3]proposesanadaptiveimageenhancementalgorithm,whichreversetheprocessingorderofimageenhancementandsegmentationinordertoavoidsharpeningnoiseandblurringborders.Munteanu[4]appliesartificialintelligencetechnologytoimageenhancementprovidingdenoisingfunction.Inadditiontospatialdomainmethods,frequencydomainprocessingtechniquesarebasedonmodifyingtheFouriertransformofanimage.Bakir[5]discussesimageenhancementusedformedicalimageprocessinginfrequencyspace.Besides,Wang[6]presentsaglobalmultiscaleanalysisofimagesbasedonHaarwavelettechniqueforimagedenoising.Recently,Agaian[7]proposesimageenhancementmethodsbasedonthepropertiesofthelogarithmictransformdomainhistogramandhistogramequalization.Weapplyspatialprocessinghereinordertoguaranteethereal-timeandsufficientaccuracypropertyofthesystem.@#@Segmentationisdiscussedin[8].Themostsimplest,representedbyOtsu[9],ismethodusingonlythegraylevelhistogramanalysistomaximizetheseparabilityoftheresultantclasses.Kuntimad[10]describesamethodforsegmentingdigitalimagesusingpulsecoupledneuralnetworks（PCNN）.Salzenstein[11]dealswithacomparisonofrecentstatisticalmodelsonfuzzyMarkovrandomfieldsandchainsformultispectralimagesegmentation.Duetoill-defined,thereisnouniquesegmentationofanimage.Evaluationofsegmentationalgorithmsthusfarhasbeenlargelysubjective.Ranjith[12]demonstrateshowarecentlyproposedmeasureofsimilaritycanbeusedtoperformaquantitativecomparisonamongimagesegmentationalgorithms.@#@Inthispaper,wepresentanimprovedspatiallow-passfilterwithatunableparameterinthemaskmakingallelementsnolongersumtounity.Theoptimalparameterforthefiltercanbedeterminedbytheimproveddiscriminantcriterionbasedontheonementionedin[9].Convolvingimageswiththismask,thebackgrounduninterestingcanberemovedeasilyleavingtheobjectintacttosomeextent.Theremainderofthepaperisorganizedasfollows:

@#@Sect.2presentshowtoenhanceaninputimageintheoryandpresentsthealgorithm.Sect.3illustratesthevalidityofthemethodinSect.2.Finally,conclusionanddiscussionarepresentedinSect.4.@#@2　ImageEnhancement@#@2.1AnalysisofPriorKnowledge@#@Thepreprocessingqualityinfluencesthelatterworkdirectly,inthat,featuredescription.Therefore,analysisforthecharacteristicsrelatedtoinputimagesshouldbepresented.AstandardimageofcrackedcontainerisshownasFig.1（a）.Fromtheimage,weseethecrackedpartoccupiessmallregion.Muchnoise,suchasrust,shadow,smearetc,appearswithinthebackground.Atacoarseglance,however,wefindgrayleveloftheholeislessthantheotherpartsdistinctly.Furtherstudyshowsgraylevelofpixel