原创版图像增强外文文献及翻译.docx
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原创版图像增强外文文献及翻译.docx
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原创版图像增强外文文献及翻译
附录A:
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.Furtherstudyshowsgraylevelofpixels,aroundtheedgeofthehole,istheminimal.Fig.1(b)displaysthehistogramofFig.1(a)andedgeoftheholeismarked.
Fig.1(a)isastandardgraylevelimageofacrackedcontainer(b)isthehistogramofFig.1(a),indicatinggraylevelregionofthehole’sedge.
2.2Formulation
Thissectiondiscussestheprincipalcontentinthepaper.Traditionalspatialfilterusesa3×3mask,theelementsofwhichsumtounity,toconvolvewiththeinputimage.Thismethodcandealwithsomecasesshowninequation
(1):
(1)
where,Iisimageinterested,NisGaussianwhitenoise,(x,y)denoteseachpairofcoordinates.NcanbedeliminatedbyblurringG.Ourobjective,however,istodeliminatenotonlywhitenoise,butanyotherbackgrounduninteresting.Thusequation
(1)isimprovedbyequation
(2):
(2)
where,I'istheobject,N'consistsofwhitenoiseandtheotherpartsexceptI'.Fig.2(c)displaysanimprovedmaskwithaparameterPara.WewilllaterillustratethattuningParaproperlyistofacilitateobjectsegmentation.Thesmoothingfunctionusedisshowninequation(3):
(3)
where,F(x,y)denotesthesmoothingfilter,inthat,themaskshownasFig.2(c).
Now,weonlyconsidergray-levelimages,anddefineMgasthemaximumgraylevelofanimage.Thenthefollowingequationsaresettodistinguishtheobjectofinterestandthenon-object:
(4)
Inessence,convolutionoperatorisalow-passfilteringprocess,whichblursanimagebyslidingamaskthroughtheimageandleavesthefilteringresponseatthepositioncorrespondingtocentrallocationofthemask.Onequestionoccursthat,whynotenhancevalueofeachpixelbythesamescaledirectlyforthedistinctgraylevelsbetweentheobjectandbackground.Thereasonisthatitdoesn’tconsidertherelationshipofadjacentpixels.Whenindividualnoisepointoccur,enhancingitsgrayvaluedirectlywillpreservethenoisepoint.Experimentsillustratethelattermethodwillleavelotsofnoisepointscan’tberemoved,buttheformermethodwillnot.
Now,wewillsearchtheoptimalparameterParasoastomaximizetheseparabilitybetweenobjectandbackground.LetagivenimageberepresentedinLgraylevels.ThenumberofpixelsatleveliisdenotedbyniandthetotalnumberofpixelsbyN.TheprobabilityofeachlevelisdenotedbyPiasfollow[9]:
(5)
SupposethatwepartitionthepixelsintotwoclassesC0andC1(objectandbackground)byathresholdatlevelk;C0denotespixelswithlevels[1,…,k],andC1denotespixelswithlevels[k+1,…,L].Thentheprobabilitiesofclassoccurrencew0,w1andtheclassmeanlevelsu0,u1respectively,aregivenby
(6)
(7)
(8)
(9)
(10)
(11)
(12)
Theprocedureofobtainingoptimalparaisbasedonobtainingoptimalthresholdforeveryfilteredimage.Theoptimalthresholdisdeterminedbymaximizingtheseparabilitybetweenobjectandbackgroundusingthefollowingdiscriminantcriterionmeasureasmentionedin[9]:
(13)
where
(14)
and
arethebetweenclassvarianceandthetotalvarianceoflevels,respectively.
(15)
Theoptimalthresholdk*thatmaximizesnisselectedinthefollowingsequentialsearchbyusingequation(5)-(14):
(16)
Equation(16)isadiscriminantcriteriontoselectthegrayleveltomaximizetheseparabilitybetweenobjectandbackgroundforagivenpicture.Inthispaper,aparameterParaisintroduced,sotheequations(6)~(9),(11)~(14),(16)isparameterizedbyParaandkandequations(10),(15)isparameterizedbyPara.Equation(13)canberewrittenas:
(17)
Where
isnotaconstantanymoreandisnotnegligible,butsomecomputationreductioncanbeoperatedon
and
Here,whatwewanttoacquireistheproperfilteredpictureincludingvividobjectbysearchingparameterPara,thediscriminantcriterionusedisimprovedasfollow:
(18)
Intheaboverepresentation,parameterParaplaysanimportantrole,becauseoptimalParamakestheseparabilitybetweenobjectandbackgroundmaximal,andmakeOtsusegmentationmethodeffectivetosegmentsmallobjectfromlargebackgroundwithoutdistinctivegray-valuebetweenthem,whichcanbeobservedlaterfromimagehistogramafterimageenhancement
2.3ExistenceDiscussionofParaandk*
Theproblemaboveisreducedtosearchforathresholdk*undertheconditionofParawhichmaximizesthediscriminantcriterioninequation(18).Theconditiondiscussedistheimagewithtwoclassatleast.Subsequently,thefollowingtwocasesdon’toccur,inthat,
(1)w0orw1iszerooriginallywithoutsettingPara,inwhichthereisonlyoneclass;
(2)w0orw1iszerowithcertainincreasingPara,inwhichthereisalsooneclassfinally;
Theabovetwocasesaredecribedas:
ThecaseconcernedisA,Thus,thereiscertainParawithproperktomakediscriminantcriterionmaximal.
3Experiments
Thispaperaimsa
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