原创版图像增强外文文献及翻译.doc
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原创版图像增强外文文献及翻译.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
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