数字图像处理中英文对照外文翻译文献

第18页中英文翻译原文ToimageedgeexaminationalgorithmresearchAbstract：Digitalimageprocessingtookarelativequiteyoungdiscipline,isfollowingthecomputertechnologyrapiddevelopment,daybydayobtainsthewidespreadapplication.Theedgetooktheimageonekindofbasiccharacteristic,inthepatternrecognition,theimagedivision,theimageintensificationaswellastheimagecompressionandsooninthedomainhasamorewidespreadapplication.Imageedgedetectionmethodmanyandvaried,inwhichbasedonbrightnessalgorithm,isstudiesthetimetobemostlong,thetheorydevelopsthematurestmethod,itmainlyisthroughsomedifferenceoperator,calculatesitsgradientbasedonimagebrightnessthechange,thusexaminestheedge,mainlyhasRobert,Laplacian,Sobel,Canny,operatorsandsoonLOG。Firstasawholeintroduceddigitalimageprocessingandtheedgedetectionsurvey,hasenumeratedseveralkindofatpresentcommonlyusededgedetectiontechnologyandthealgorithm,andselectstwokindstouseVisualtheClanguageprogrammingrealization,throughwithdrawstheimageresulttotwoalgorithmsthecomparison,theresearchdiscussestheirgoodandbadpoints.Foreword：Inimageprocessing,asabasiccharacteristic,theedgeoftheimage,whichiswidelyusedintherecognition,segmentation,intensificationandcompressoftheimage,isoftenappliedtohigh-leveldomain.Therearemanykindsofwaystodetecttheedge.Anyway,therearetwomaintechniques:oneisclassicmethodbasedonthegraygradeofeverypixel;theotheroneisbasedonwaveletanditsmulti-scalecharacteristic.Thefirstmethod,whichisgotthelongestresearch,gettheedgeaccordingtothevarietyofthepixelgray.ThemaintechniquesareRobert,Laplace,Sobel,CannyandLOGalgorithm.Thesecondmethod,whichisbasedonwavelettransform,utilizestheLipschitzexponentcharacterizationofthenoiseandsingularsignalandthenachievethegoalofremovingnoiseanddistillingtherealedgelines.Inrecentyears,anewkindofdetectionmethod,whichbasedonthephaseinformationofthepixel,isdeveloped.Weneedhypothesizenothingaboutimagesinadvance.Theedgeiseasytofindinfrequencydomain.It’sareliablemethod.Inchapterone,wegiveanoverviewoftheimageedge.Andinchaptertwo,someclassicdetectionalgorithmsareintroduced.Thecauseofpositionalerrorisanalyzed,andthendiscussedamoreprecisionmethodinedgeorientation.Inchapterthree,wavelettheoryisintroduced.Thedetectionmethodsbasedonsamplingwavelettransform,whichcanextractmaimedgeoftheimageeffectively,andnon-samplingwavelettransform,whichcanremaintheoptimumspatialinformation,arerecommendedrespectively.Inthelastchapterofthisthesis,thealgorithmbasedonphaseinformationisintroduced.UsingthelogGaborwavelet,two-dimensionfilterisconstructed,manykindsofedgesaredetected,includingMachBand,whichindicatesitisaoutstandingandbio-simulationmethod。Mayalltheworkinthispaperisofsomevaluetoresearchandapplicationsofimageedgedetection.Firstchapterintroduction§1.1imageedgeexaminationintroductionTheimageedgeisoneofimagemostbasiccharacteristics,ofteniscarryingimagemajorityofinformations。Buttheedgeexistsintheimageirregularstructureandinnotthesteadyphenomenon,alsonamelyexistsinthesignalpointofdiscontinuityplace,thesespotshavegiventheimageoutlineposition,theseoutlinesarefrequentlywewhentheimageryprocessingneedstheextremelyimportantsomerepresentativecondition,thisneedsustoexamineandtowithdrawitsedgetoanimage。Buttheedgeexaminationalgorithmisintheimageryprocessingquestiononeofclassicaltechnicaldifficultproblems,itssolutioncarriesonthehighlevelregardingusthecharacteristicdescription,therecognitionandtheunderstandingandsoonhasthesignificantinfluence;Alsobecausetheedgeexaminationallhasinmanyaspectstheextremelyimportantusevalue,thereforehowthepeoplearedevotingcontinuouslyinstudyandsolvethestructuretoleavehavethegoodnatureandthegoodeffectedgeexaminationoperatorquestion。Intheusualsituation,wemaythesignalinsingularpointandthepointofdiscontinuitythoughtisintheimageperipheralpoint,itsnearbygradationchangesituationmayreflectfromitsneighboringpictureelementgradationdistributiongradient。Accordingtothischaracteristic,weproposedmanykindsofedgeexaminationoperator:IfRobertoperator,Sobeloperator,Prewittoperator,Laplaceoperatorandsoon.Thesemethodsmanyarewaitfortheprocessingpictureelementtocarryonthegradationanalysisforthecentralneighborhoodachievementthefoundation,realizedandhasalreadyobtainedthegoodprocessingeffecttotheimageedgeextraction.。Butthiskindofmethodsimultaneouslyalsoexistshastheedgepictureelementwidth,thenoisejammingisseriousandsoontheshortcomings,evenifusessomeauxiliarymethodstoperformthedenoising,alsocorrespondingcanbringtheflawwhichtheedgefuzzyandsoonovercomeswithdifficulty。Alongwiththewaveletanalysisappearance，itsgoodtimefrequencypartialcharacteristicbythewidespreadapplicationintheimageryprocessingandinthepatternrecognitiondomain,becomesinthesignalprocessingthecommonlyusedmethodandthepowerfultool。Throughthewaveletanalysis,mayinterweavedecomposesinthesameplaceeachkindofcompositesignalthedifferentfrequencytheblocksignal，butcarriesontheedgeexaminationthroughthewavelettransformation,mayuseitsmulti-criteriaandthemulti-resolutionnaturefully，realeffectiveexpressestheimagetheedgecharacteristic。Whenthewavelettransformationcriterionreduces,ismoresensitivetotheimagedetail;Butwhenthecriterionincreases,theimagedetailisfilteredout,theexaminationedgewillbeonlythethickoutline.Thischaracteristicisextremelyusefulinthepatternrecognition,wemaybecalledthisthickoutlinetheimagethemainedge.Ifwillbeableanimagemainedgeclearintegrityextraction,thistothegoaldivision,therecognitionandsoonfollowingprocessingtobringtheenormousconvenience.Generallyspeaking,theabovemethodallistheworkwhichdoesbasedontheimageluminanceinformation。Inthemultitudinousscientificresearchworkerunder,hasobtainedtheverygoodeffectdiligently.But,becausetheimageedgereceivesphysicalconditionandsoontheilluminationinfluencesquitetobebigabove,oftenenablesmanytohaveacommonshortcomingbasedonbrightnessedgedetectionmethod,thatistheedgeisnotcontinual,doesnotsealup.Consideredthephaseinformationintheimageimportanceaswellasitsstablecharacteristic,causesusingthephaseinformationtocarryontheimageryprocessingintonewresearchtopic。Inthispapersoonintroducesonekindbasedonthephaseimagecharacteristicexaminationmethod--phaseuniformmethod.Itisnotusestheimagetheluminanceinformation,butisitsphasecharacteristic,namelysuppositionimageFouriercomponentphasemostconsistentspotachievementcharacteristicpoint.Notonlyitcanexaminebrightnesscharacteristicsandsoonstepcharacteristic,linecharacteristic,moreovercanexamineMachbeltphenomenonwhichproducesasaresultofthehumanvisionsensationcharacteristic.Becausethephaseuniformitydoesnotneedtocarryonanysuppositiontotheimagecharacteristictype,thereforeithastheverystrongversatility。§1.2imageedgedefinitionTheimagemajoritymaininformationallexistsintheimageedge,themainperformancefortheimagepartialcharacteristicdiscontinuity,isintheimagethegradationchangequitefierceplace,alsoisthesignalwhichweusuallysaidhasthestrangechangeplace。Thestrangesignalthegradationchangewhichmovestowardsalongtheedgeisfierce,usuallywedividetheedgeforthestepshapeandtheroofshapetwokindoftypes(asshowninFigure1-1).Inthestepedgetwosidegreylevelshavetheobviouschange;Buttheroofshapeedgeislocatedthegradationincreaseandthereducedintersectionpoint.Mayportraytheperipheralpointinmathematicsusingthegradationderivativethechange,tothestepedge,theroofshapeedgeasksitsstep,thesecondtimederivativeseparately。Toanedge,hasthepossibilitysimultaneouslytohavethestepandthelineedgecharacteristic.Forexampleonasurface,changesfromaplanetothenormaldirectiondifferentanotherplanecanproducethestepedge;Ifthissurfacehastheedgesandcornerswhichtheregularreflectioncharacteristicalsotwoplanesformquitetobesmooth,thenworksaswhenedgesandcornerssmoothsurfacenormalaftermirrorsurfacereflectionangle,asaresultoftheregularreflectioncomponent,canproducethebrightlightstripontheedgesandcornerssmoothsurface,suchedgelookedlikehaslikelysuperimposedalineedgeinthestepedge.Becauseedgepossibleandinsceneobjectimportantcharacteristiccorrespondence,thereforeitistheveryimportantimagecharacteristic。Forinstance,anobjectoutlineusuallyproducesthestepedge,becausetheobjectimageintensityisdifferentwiththebackgroundimageintensity。§1.3paperselectedtopictheorysignificanceThepaperselectedtopicoriginatesinholdstheimportantstatusandthefunctionpracticalapplicationtopicintheimageproject.Theso-calledimageprojectdisciplineisrefersfoundationdisciplineandsoonmathematics,opticsprinciples,thedisciplinewhichintheimageapplicationunifieswhichaccumulatesthetechnicalbackgrounddevelops.Theimageprojectcontentisextremelyrich,andsoondividesintothreelevelsdifferentlyaccordingtotheabstractdegreeandtheresearchtechnique:Imageryprocessing,imageanalysisandimageunderstanding。AsshowninFigure1-2,inthechart,theimagedivisionisinbetweentheimageanalysisandtheimageryprocessing,itsmeaningis,theimagedivisionisfromtheimageryprocessingtotheimageanalysisessentialstep,alsoisfurtherunderstandstheimagethefoundation。Theimagedivisionhastheimportantinfluencetothecharacteristic.Theimagedivisionandbasedonthedivisiongoalexpression,thecharacteristicextractionandtheparametersurveyandsoontransformstheprimitiveimageasamoreabstractmorecompactform,causesthehigh-levelimageanalysisandpossiblyunderstandsinto.Buttheedgeexaminationistheimagedivisioncorecontent,thereforetheedgeexaminationholdstheimportantstatusandthefunctionintheimageproject.Thereforetheedgeexaminationresearchalwaysisintheimageengineeringresearchthehotspotandthefocalpoint,moreoverthepeopleenhanceunceasinglytoitsattentionandtheinvestment。§1.4thisarticleprimetasks§1.4.1AlgorithmcontentIntroducedandhasanalyzedtheclassicsimageedgeexaminationalgorithm,summarizedeachalgorithmgoodandbadpoints,hasgiventheimageedgeexaminationresult,andemphaticallytaketheLOGalgorithmastheexample,embarkedfromthenoiseandtheedgeshapeviewpointhasanalyzedthereasonwhichtheedgepositionerrorproduced;IntroducedinonekindofLOGalgorithmthequiteprecisedefiniteedgemethod。§1.4.2WavelettheoryHasstudiedthewaveletelementarytheory,summarizedthesignalaswellasthenoiseLipindexnature,andinbasedoninthenon-samplingwavelettransformationimagecharacteristicextractionalgorithmfoundation,unifiestheauto-adapteddenoisingmethod,hasmadecertainimprovementtothismethod,obtainedthequitesatisfactoryeffect,denoisingabilityhadthequitebigenhancement;Thenintroducedonekindbasedonthesamplingwaveletexaminationimagemainedgemethod。§1.4.3NovelalgorithmThesystemhasstudiedonekindquitenovelbasedonthephaseimagecharacteristicextractionalgorithm--phaseuniformalgorithm,andhasgivenitssimplealgorithm.Hasgivenintheunidimensionalsituationalgorithmsimulationstep,analyzedexpandedtothetwo-dimensionalmethod,andexplainedbytheedgeexaminationresultthephaseuniformalgorithmconformedtothehumanvisioncharacteristic。§1.5thisarticlecontentarrangementInthefirstchapter,theauthorhasgivenanoutlineexplanationtotheimageedgeexamination,andexplainedcarriesontheimageedgeexaminationthevitalsignificance.Insecondchapter,thesystemintroducedthequiteclassicalimageedgeexaminationoperatorandtheconcreterealizationprinciple,haveanalyzedeachalgorithmexistenceinsufficiencybytheedgeexaminationresult.Finally,fromthenoiseinfluenceandedgeshapeobtaining,taketheLOGalgorithmastheexample,hasanalyzedthereasonwhichthefalseedgeaswellasedgeshiftingproduces.FinallyintroducedinonekindofLOGalgorithmthequiteprecisedefiniteedgemethod.Inthirdchapter,theauthorsystemintroducedthepresentquitepopularwavelettheory,andintroducedemphaticallythemulti-criterionconceptandthesignalLipindex,andbythenoiseandthesignalLipindexcharacteristic,carriesontheextractioninthenon-samplingwavelettransformationfoundationtotheimageedge.Inordertostrengthentheedgeimageanti-chirpability,butalsothealgorithmhasmadecertainimprovementregardingthis,theauto-adapteddenoisingmethodwilluseintheedgedetection,hasobtainedthesatisfyingeffect.Finallyalsointroducesonekindbasedonthesamplingwaveletexaminationimagemainedgemethod.Inthethisarticlefourthchapter,introducedonekindquitenovelbasedonthephaseimagecharacteristicextractionalgorithm--phaseuniformalgorithm.Fromunidimensionalalgorithmintroductionobtaining,hasgivenundertheunidimensionalsignalsimulationresult,andexpandsgraduallytwo-dimensionally.Explainedthroughthesimulationresultthisalgorithmrobustnessquiteisstrong,moreoverconformstohumanity'svisualsystemperformance.SecondchapterclassicalimageedgeexaminationalgorithmThischapterfirstsimplyintroducedaclassicsstepedgeexaminationessentialmethodin2.1.Then2.2and2.3distinctionselaboratedspecificallytheclassicalderivativeoperatorandthelinearfilteringoperatorrealizationmethod,andhasgiveneachalgorithmresultcomparisonin2.4.In2.5,comparedwiththeconcreteanalysisnoiseandtheedgeshapethereasonwhichproducedtotheedgepointingaccuracyinfluenceaswellasthefalseedge,andhasgivenintheunidimensionalsituationsimulationresult,hasdrawntheconclusion.In2.6,theimagepositiveandnegativeedgewhichpicksoutusingtheLOGalgorithm,comparedwiththepreciselocalizationimagerealedge,thefinaloutputwastwovaluesinglepictureelementimage.§2.1classicaledgeexaminationessentialmethodWeknewthat,theedgeexaminationessenceisusessomealgorithmtowithdrawintheimagetheobjectandthebackgroundjunctiondemarcationline.Wedefinetheedgefortheimageinthegradationoccurtherapidchangeregionboundary.Theimagegradationchangesituationmayusetheimagegradationdistributionthegradienttoreflect,thereforewemayusethepartialimagedifferentialtechnologytoobtaintheedgeexaminationoperator.Theedgeexaminationalgorithmhasthefollowingfoursteps(itsprocessasshowninFigure2-1):Filter:Theedgeexaminationalgorithmmainlyisbasedonanimageintensitystepandthesecondtimederivative,butthederivativecomputationisverysensitivetothenoise,thereforemustusethefiltertoimproveandthenoiserelatededgedetectorperformance.Needstopointoutthat,themajorityfilterhavealsocausedtheedgeintensitylosswhilenoisereduction,therefore,strengthenstheedgeandbetweenthenoisereductionneedscompromised.Enhancement:Strengthenstheedgethefoundationisdeterminestheimageeachneighborhoodintensitythechangevalue.Theenhancementalgorithmmay(orpartial)theintensityvaluehastheneighborhoodtheremarkablechangespottorevealsuddenly.Theedgestrengthensisgenerallycompletesthroughthecomputationgradientpeak-to-peakvalue.Examination:Hasmanypointgradientpeak-to-peakvalueintheimagequitetobebig,buttheseinthespecificapplicationdomainnotallistheedge,thereforeshouldusesomemethodtodeterminewhichselectistheperipheralpoints.Thesimpleedgeexaminationcriterionisthegradientpeak-to-peakvaluethresholdvaluecriterion.Localization:Ifsomeapplicationsituationrequestdefiniteedgeposition,thentheedgepositionmaycomeuptheestimateinthesub-pictureelementresolution,theedgepositionalsomayestimate.Intheedgeexaminationalgorithm,thefirstthreestepsuseextremelyuniversally.Thisisbecauseunderthemajoritysituations,needstheedgedetectortopointoutmerelytheedgeappearsinimagesomepictureelementneighbor,butisnotunnecessarytopointouttheedgetheexactlocationorthedirection.Theedgeexaminestheerrorusuallyisreferstotheedgetoclassifytheerrorbymistake,namelydistinguishedthevacationedgetheedgeretains,butdistinguishedtherealedgethevacationedgeremoves.Theedgeerrorofestimationisdescribestheedgepositionandthelateralerrorwiththeprobabilitystatisticalmodel.Weexaminetheedgetheerrorandtheedgeerrorofestimationdifferentiate,isbecausetheircomputationalmethodiscompletelydifferent,itserrormodelcompletelyisalsodifferent.Theedgeexaminationisexaminestheimagepartialremarkablechangethemostfundamentaloperation.Intheunidimensionalsituation,thestepedgeconcernswiththeimagefirstderivativepartialpeakvalue.Thegradientisthefunctionchangeonekindofmeasure,butanimagemayregardasistheimageintensitycontinuousfunctionsamplingpointarray.Therefore,issimilarwiththeunidimensionalsituation,theimagegreylevelremarkablechangeavailablegradientdiscreteapproximationfunctionexamines.Thegradientisfirstderivativetwo-dimensionalequivalent-like,definesforthevectorasaresultofeachkindofreason,theimagealwaysreceivesthestochasticnoisethedisturbance,maysaythenoiseisubiquitous.Becausetheclassicaledgeexaminationmethodhasintroducedeachformdifferentiate,thuscausesinevitablytothenoiseextremelysensitive,carriesouttheedgeexaminationresultisfrequentlyexaminesthenoiseregardperipheralpoint,butbutthegenuineedgealsoasaresultofreceivesthenoisejammingnottoexamine.Thusregardinghasthenoiseimage,onegoodedgeexaminationmethodshouldhavethegoodnoiseabatementability,simultaneouslyalsohasthecompleteedgemaintenancecharacteristic。2-11looksbyFigureplace,doesnothaveinthenoisesituationintheimage,thePrewittoperator,theRobertoperator,theSobeloperatoraswellasthedifferentialgradientoperator,allcanthequiteaccurateexaminationedge.But,afterjoinsthewhitegaussiannoise,theRobertoperatorreceivestheinfluenceissmallest,nextisthePrewittoperator,receivesaffectsinabigwayistheSobeloperator,butregardingthedifferentialgradientoperator,thenistheimageoverallcontrastgradienthasobviousdepression.2-17mayseebyFigure,indoesnothaveinthenoisesituation,theCannyoperator,theLOGoperatorandtheLaplaceoperatorallmayobtainthequitegoodexaminationeffect,but,theLOGoperatoralwayscanproducethefalseedge,thisanditszerocrossingexaminationmethodconcerns.Afteraddsonthenoise,traditionalexaminationoperator(Laplaceoperator)theexaminationqualitydroppedobviously,buttheLOGoperatorhasproducedmorefalseedgesunderthenoisecondition.ButtheCannyoperatorexaminationresultiscontinuouslyextremelysatisfying.Aboutthenoisetotheedgealgorithminfluence,weinthenextsection,taketheLOGalgorithmastheexample,makesthequiteexhaustiveanalysisandtheelaboration.Thischapterfirstfromthetraditionbasedonimagegradationfirstderivativeedgeobtaining,introducedtheclassicsedgeexaminationoperatoraswellasafterwardsdevelopedthelinearfilteringedgeexaminationmethod,comparedwiththeyrespectivecharacteristic,aswellasineachkindofnoisesituationexaminationability,andhasgiventhesimulationresult,finallydiscovered,theCannyoperatorandwithdrewtheedgeaspectinthenoiseeliminationtohavethequitegoodeffect.Inthischapterfinal,butalsotaketheLOGoperatorastheexample,hasanalyzedtheimagenoiseandtheedgeshapetoitsexaminationresultanalysis,andhasgiventhesimulationresult,explainedunderthetraditionalfixedcriterionexaminationmethod,theimageedgesymmetryaswellasthecriterionchoicetoexaminestheresulttohavetheverytremendousinfluence.Finally,weaccordingtoAmlantheKundurecommendation20kindoftemplateshapes,examinesintheLOGalgorithmintheedgeimage,withdrawsthepositiveandnegativeboundarythecentralposition,takesourfinaltwovaluesinglepictureelementboundaryoutput.ThirdchapterbasedonwaveletthoughtimageedgeexaminationAlthoughtheedgedetectionhadthedifferentialgradientoperator,theLaplaceoperator,theSobeloperator,theLOGoperatoraswellastheCannyoperatorandsoonmanymethods,butthesealgorithmsdonothavetheautomaticfocalvariationthought.Butinfact,asaresultofreasonsandsoonphysicsandillumination,ineachimageedgeusuallyproducesinthedifferentcriterionscope,formsthedifferenttypetheedge(forexampleedgesandsoonstep,roof),theseinformationsareunknown.Moreover,intheimagealwayshasthenoise,therefore,accordingtotheimagecharacteristic,canauto-adaptedexaminetheimagetheedgeiscorrectlyextremelydifficult.Easytoimagine,isnotimpossibletoexaminealledgeswiththesolecriterionedgeexaminationoperator,simultaneously,foravoidsaffectingtheedgeexaminationduringfiltrationnoisetheaccuracy,examinestheedgewiththemulti-criterionmethodmoreandmoretobringtopeople'sattention.Becausethewavelettransformationhasthegoodtimefrequencylocalizationcharacteristicaswellasmulti-criterionanalysisability[10,11,12],has“thefocalvariation”inthedifferentcriterionthefunction,suitsintheexaminationsuddenchangesignal,isexaminesthiskindofsignalthepowerfultool,thereforeobtainedthewidespreadapplication.Thischapterontakethewavelettransformationasthefoundation,aftertheusewaveletfiltertheimagecharacteristic,insamplingandinthenon-samplingfoundation,theexaminationimageedge,andgavesomeimprovementcomment.Basedonthenon-samplingwavelettransformationedgedetection,thespatialinformationwhichthemaintenanceoptimizes,hasobtainedthequitegoodeffect,but,whenwemerelyareinterestedtotheimageapproximateoutlinetime,thesamplingwavelettransformpairweareperhapsmorepractical.ThesamplingwavelettransformationistheMallattowersystemalgorithmwhichthefaceexplainsthingsinfrontofthischaptermentioned,itcanobtaintheprimitiveimageindifferentcriteriondetail.Whenthedecompositioncriterionincreases,notonlyhasfilteredthenoise,butalsowithdrewtheimageapproximateoutline.Wemayuseunderthegreatcriteriontheedgeimagetoinstructthesmallcriterionlowerlimbtheextraction.Thus,hasremovedthesmalldetailwhichtheverysensitivemajorityofnoisesandweisnotcaredaboutverymuchunderthesmallcriterion,isonlytheimagemainoutlinewhichretainsustocareabout.ItsflowchartasshowninFigure3-7.Mustpayattentioninhere,thetransformationcriteriondonothavetootobebig,otherwisemadetheinstructiontheedgetootobeshort,easytocreatetheedgeinformationtolosetoomany,theoutlinewasstiff,wasnotgentle.Andafterthedecompositioncriterionexcessivelyaremany,ashiftingcancauseslightlyinthegreatcriterioninthesmallcriteriontheverybigmistake.Amongthem,thisalgorithmdifficultyprovidestheinstructioninthegreatcriterionedgeforthesmallcriterionthepart.Becausethegreatcriterionedgewillbesamplinglatertheobtaineddata,thereforeaboveitinapointcorrespondenceandsmallcriterionfourspots.Ifinsmallcriterionfourspotintensityenoughbig,weondetermineitastheboundarypoint.ThisalgorithmsimulationresultasshowninFigure3-8.Inhere,ourhavenotusedthewaveletinversetransformationwhichintheusualmethoduses,but,hasstillobtainedthequitegoodresult.§3.5thischaptersectionThischapterinafterintroductionwaveletedgeexaminationprinciple,application,introducedusesinscoringthesignalirregularityLipindex,andexplainedundercertaincondition,alsomayestimatefunctionf(t)withthewavelettransformationinat0Lipindex.Thus,relatedthewavelettransformationandthesignalLipindex.Whencarriesontheedgeexaminationusingthewavelet,appliesnon-samplingthewavelettransformation,andhasmadesomeimprovementstothetraditionalmethod,unifiestheauto-adaptedfiltermethodwiththesignalirregularityexamination,inthenoisequitebigsituation,hasobtainedthequitesatisfyingdenoisingeffect.Inthischapterfinal,butalsodiscussedinthesamplingwavelettransformationfoundation,carriesontheimagethemainedgeextractionmethod.Thismethodistakeswiththegreatcriterionunderedgeimageinstructsunderthesmallcriteriontheedgeimagetocarryonthechoices,thesimulationresultindicatedthismethodiseffective.FourthchapterbasedonphaseinformationimageedgeexaminationalgorithmWeknewthat,theimagecharacteristicexaminationisimageryprocessing,patternrecognition,basedoncontentdomainandsoonimageretrievalkeytechnologies.Howexaminesaswellastheeffectivedescriptionimagecharacteristic,sincelongagoreceivesthemultitudinousdisciplinescontinuouslytheattention.Theoverwhelmingmajorityimagecharacteristicexaminationalgorithmalliscarriesonbasedontheimagebrightnessgradient.Notesthephaseinformationtheimportanceandthestability,causestocarryontheimageryprocessingintonewresearchtopicusingthephaseinformation.Thischapter,wewilllaunchthediscussiononthisquitenoveltopic,willinferitsrationale,andexplainedbyitssimulationresultbasedonthephasecharacteristicextractionalgorithmsuperiority.§4.1signalphaseinformationWeknewthat,classi