數(shù)字圖像處理英文原版及翻譯

DigitalImageProcessingandEdgeDetectionDigitalImageProcessingInterestindigitalimageprocessingmethodsstemsfromtwoprincipalapplicationareas:improvementofpictorialinformationforhumaninterpretation;andprocessingofimagedataforstorage,transmission,andrepresentationforautonomousmachineperception.Animagemaybedefinedasatwo-dimensionalfunction,f(x,y),wherexandyarespatial(plane)coordinates,andtheamplitudeoffatanypairofcoordinates(x,y)iscalledtheintensityorgrayleveloftheimageatthatpoint.Whenx,y,andtheamplitudevaluesoffareallfinite,discretequantities,wecalltheimageadigitalimage.Thefieldofdigitalimageprocessingreferstoprocessingdigitalimagesbymeansofadigitalcomputer.Notethatadigitalimageiscomposedofafinitenumberofelements,eachofwhichhasaparticularlocationandvalue.Theseelementsarereferredtoaspictureelements,imageelements,pixels,andpixels.Pixelisthetermmostwidelyusedtodenotetheelementsofadigitalimage.isionisthemostadvancedofoursense,soitisnotsurprisingthatimagesplaythesinglemostimportantroleinhumanperception.Howeve,unlikehuman,whoarelimitedtothevisualbandoftheelectromagnetic(EM)spec-trum,imagingmachinescoveralmosttheentireEMspectrum,rangingfromgammatoradiowave.heycanoperateonimagesgeneratedbysourcesthathumansarenotaccustomedtoassociatingwithimage.heseincludeultra-,n,dd.,lprocessingencompassesawideandvariedfieldofapplication.hereisnogeneralagreementamongauthorsregardingwhereimageprocessingstopsandotherrelatedarea,suchasimageanalysisandcomputervi-sion,start.Sometimesadistinctionismadebydefiningimageprocessingasaenhhetdtfase.ethistobealimitingandsomewhatartificialboundar.orexampl,underthisdefinition,eventhetrivialtaskofcomputingtheaverageintensityofanimagehsae)dtednegeration.Ontheotherhand,therearefieldssuchascomputervisionwhoseultimategoalistousecomputerstoemulatehumanvision,includinglearningandbeingabletomakeinferencesandtakeactionsbasedonvisualinput.hisareaitselfisabranchofartificialintelligence(AI)whoseobjectiveistoemuen.edfIsnstsfynofdevelopment,withprogresshavingbeenmuchslowerthanoriginallyanticipated.heareaofimageanalysis(alsocalledimageunderstanding)isinbe-tweenimageprocessingandcomputervision.Basedontheprecedingcomment,weseethatalogicalplaceofoverlapbetweenimageprocessingandimageanalysisistheareaofrecognitionofindividualregionsorobjectsinanimag.hu,whatwecallinthisbookdigitalimageprocessingencompassesprocesseswhoseinputsandoutputsareimagesand,inaddition,encompassesprocessesthatextractattributesfromimage,uptoandincludingtherecognitionofindividualobject.Asasimpleillustrationtoclarifytheseconcept,considertheareaofautomatedanalysisoftext.heprocessesofacquiringanimageoftheareacontainingthetext,preprocessingthatimag,extracting(segmenting)theindividualcharacter,describingthecharactersinaformsuitableforcomputerprocessin,andrecognizingthoseindividualcharactersareinthescopeofwhatwecalldigitalimageprocessinginthisbook.Makingsenseofthecontentofthepagemaybeviewedasbeinginthedomainofimageanalysisandevencomputervision,dependingonthelevelofcomplexityimpliedbythestatement“makingsens”Aswillbecomeevidentshortl,digitalimageprocessin,aswehavedefinedit,isusedsuccessynadefsflldc.esfnflegeodtformoforganizationisdesirableinattemptingtocapturethebreadthofthis.efetsopacgfetegssoesgor,,,do.elyersnesecy.rtsfye,,dcnemfnsdn.c,drgd,dy.nsnesywsednessdesnhye.ImagesbasedonradiationfromtheEMspectrumarethemostfamiliar,especiallyimagesintheX-rayandvisualbandsofthespectrum.Electromagnet-icwavescanbeconceptualizedaspropagatingsinusoidalwavesofvaryingwavelengths,ortheycanbethoughtofasastreamofmasslessparticles,eachtravelinginawavelikepatternandmovingatthespeedoflight.Eachmasslessparticlecontainsacertainamount(orbundle)ofenergy.Eachbundleofenergyiscalledaphoton.Ifspectralbandsaregroupedaccordingtoenergyperphoton,weobtainthespectrumshowninfig.below,rangingfromgammarays(highestenergy)atoneendtoradiowaves(lowestenergy)attheother.ThebandsareshownshadedtoconveythefactthatbandsoftheEMspectrumarenotdistinctbutrathertransitionsmoothlyfromonetotheother.Imageacquisitionisthefirstprocess.Notetndesesgnnetsynitalform.Generall,theimageacquisitionstageinvolvespreprocessin,suchasscalin.Imageenhancementisamongthesimplestandmostappealingareasofdigitalimageprocessin.Basicall,theideabehindenhancementtechniquesistogtlts,ryotnsfinanimag.Afamiliarexampleofenhancementiswhenweincreasethecontrastofanimagebecause“itlooksbette”Itisimportanttokeepinmindthattsayeafe.Imagerestorationisanareathatalsodealswithimprovingtheappearanceofanimag.Howeve,unlikeenhancement,whichissubjectiv,imagerestorationisobjectiv,inthesensethatrestorationtechniquestendtobebasedonmathematicalorprobabilisticmodelsofimagedegradation.Enhancement,ontheotherhand,isbasedonhumansubjectivepreferencesregardingwhatconstitutesa“good”enhancementresult.ColorimageprocessingisanareathathasbeengaininginimportancebecauseofthesignificantincreaseintheuseofdigitalimagesovertheInternet.Itcoversanumberoffundamentalconceptsincolormodelsandbasiccolorprocessinginadigitaldomain.Colorisusedalsoinlaterchaptersasthebasisforextractingfeaturesofinterestinanimag.aveletsarethefoundationforrepresentingimagesinvariousdegreesofresolution.Inparticula,thismaterialisusedinthisbookforimagedatacomndrl,nhsedcessivelyintosmallerregion.Compression,asthenameimplies,dealswithtechniquesforreducingthestoragerequiredtosaveanimage,orthebandwidthrequiredtotransmitit.Althoughstoragetechnologyhasimprovedsignificantlyoverthepastdecade,thesamecannotbesaidfortransmissioncapacity.ThisistrueparticularlyinusesoftheInternet,whicharecharacterizedbysignificantpictorialcontent.Imagecompressionisfamiliar(perhapsinadvertently)tomostusersofcomputersintheformofimagefileextensions,suchasthejpgfileextensionusedintheJPEG(JointPhotographicExpertsGroup)imagecompressionstandard.Morphologicalprocessingdealswithtoolsforextractingimagecomponentsthatareusefulintherepresentationanddescriptionofshap.hematerialinthischapterbeginsatransitionfromprocessesthatoutputimagestoprocessesthatoutputimageattribute.Segmentationprocedurespartitionanimageintoitsconstituentpartsorobject.Ingeneral,autonomoussegmentationisoneofthemostdifficulttasksindigitalimageprocessin.Aruggedsegmentationprocedurebringstheprocessalongwaytowardsuccessfulsolutionofimagingproblemsthatrequireobjectstobeidentifiedindividuall.Ontheotherhand,weakorerraticsegmentationalgorithmsalmostalwaysguaranteeeventualfailur.Ingeneral,themoreaccuratethesegmentation,themorelikelyrecognitionistosucceed.Representationanddescriptionalmostalwaysfollowtheoutputofasegmentationstag,whichusuallyisrawpixeldata,constitutingeithertheboundaryofaregion(i..,thesetofpixelsseparatingoneimageregionfromanother)orallthepointsintheregionitsel.Ineithercas,convertingthedatatoaformsuitableforcomputerprocessingisnecessar.hefirstdecisionthatmustbemadeiswhetherthedatashouldberepresentedasaboundaryorasacompleteregion.Boundaryrepresentationisappropriatewhenthefocusisonexternalshapecharacteristic,suchascornersandinflection.Regionalrepresentationisappropriatewhenthefocusisoninternalpropertie,suchastextureorskeletalshap.Insomeapplication,theserepresentationscompleth.gansytfenrformingrawdataintoaformsuitableforsubsequentcomputerprocessin.Amethodmustalsobespecifiedfordescribingthedatasothatfeaturesofinterestarehighlighted.Description,alsocalledfeatureselection,dealswithextractingattributesthatresultinsomequantitativeinformationofinterestorarebasicfordifferentiatingoneclassofobjectsfromanothe.Recognitionistheprocessthatassignsalabel(..,“vehicle”)toanobjectbasedonitsdescriptor.Asdetailedbefore,weconcludeourcoveragefleghetfsrnindividualobject.SofarwehavesaidnothingabouttheneedforpriorknowledgeorabouttheinteractionbetweentheknowledgebaseandtheprocessingmodulesinFig2above.Knowledgeaboutaproblemdomainiscodedintoanimageprocessingsystemintheformofaknowledgedatabase.Thisknowledgemaybeassimpleasdetailingregionsofanimagewheretheinformationofinterestisknowntobelocated,thuslimitingthesearchthathastobeconductedinseekingthatinformation.Theknowledgebasealsocanbequitecomplex,suchasaninterrelatedlistofallmajorpossibledefectsinamaterialsinspectionproblemoranimagedatabasecontaininghigh-resolutionsatelliteimagesofaregioninconnectionwithchange-detectionapplications.Inadditiontoguidingtheoperationofeachprocessingmodule,theknowledgebasealsocontrolstheinteractionbetweenmodules.ThisdistinctionismadeinFig2abovebytheuseofdouble-headedarrowsbetweentheprocessingmodulesandtheknowledgebase,asopposedtosingle-headedarrowslinkingtheprocessingmodules.EdgedetectionEdgedetectionisaterminologyinimageprocessingandcomputervision,particularlyintheareasoffeaturedetectionandfeatureextraction,torefertoalgorithmswhichaimatidentifyingpointsinadigitalimageatwhichtheimagebrightnesschangessharplyormoreformallyhasdiscontinuities.Althoughpointandlinedetectioncertainlyareimportantinanydiscussiononsegmentation,edgedetectionisbyfarthemostcommonapproachfordetectingmeaningfuldiscountiesingraylevel.Althoughcertainliteraturehasconsideredthedetectionofidealstepedges,theedgesobtainedfromnaturalimagesareusuallynotatallidealstepedges.Insteadtheyarenormallyaffectedbyoneorseveralofthefollowingeffects:1.focalblurcausedbyafinitedepth-of-fieldandfinitepointspreadfunction;2.penumbralblurcausedbyshadowscreatedbylightsourcesofnon-zeroradius;3.shadingatasmoothobjectedge;4.localspecularitiesorinterreflectionsinthevicinityofobjectedges.Atypicaledgemightforinstancebetheborderbetweenablockofredcolorandablockofyellow.Incontrastaline(ascanbeextractedbyaridgedetector)canbeasmallnumberofpixelsofadifferentcoloronanotherwiseunchangingbackground.Foraline,theremaythereforeusuallybeoneedgeoneachsideoftheline.Toillustratewhyedgedetectionisnotatrivialtask,letusconsidertheproblemofdetectingedgesinthefollowingone-dimensionalsignal.Here,wemayintuitivelysaythatthereshouldbeanedgebetweenthe4thand5thpixels.

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Iftheintensitydifferenceweresmallerbetweenthe4thandthe5thpixelsandiftheintensitydifferencesbetweentheadjacentneighbouringpixelswerehigher,itwouldnotbeaseasytosaythatthereshouldbeanedgeinthecorrespondingregion.Moreover,onecouldarguethatthiscaseisoneinwhichthereareseveraledges.Hence,tofirmlystateaspecificthresholdonhowlargetheintensitychangebetweentwoneighbouringpixelsmustbeforustosaythatthereshouldbeanedgebetweenthesepixelsisnotalwaysasimpleproblem.Indeed,thisisoneofthereasonswhyedgedetectionmaybeanon-trivialproblemunlesstheobjectsinthesceneareparticularlysimpleandtheilluminationconditionscanbewellcontrolled.Therearemanymethodsforedgedetection,butmostofthemcanbegroupedintotwocategories,search-basedandzero-crossingbased.Thesearch-basedmethodsdetectedgesbyfirstcomputingameasureofedgestrength,usuallyafirst-orderderivativeexpressionsuchasthegradientmagnitude,andthensearchingforlocaldirectionalmaximaofthegradientmagnitudeusingacomputedestimateofthelocalorientationoftheedge,usuallythegradientdirection.Thezero-crossingbasedmethodssearchforzerocrossingsinasecond-orderderivativeexpressioncomputedfromtheimageinordertofindedges,usuallythezero-crossingsoftheLaplacianofthezero-crossingsofanon-lineardifferentialexpression,aswillbedescribedinthesectionondifferentialedgedetectionfollowingbelow.Asapre-processingsteptoedgedetection,asmoothingstage,typicallyGaussiansmoothing,isalmostalwaysapplied(seealsonoisereduction).Theedgedetectionmethodsthathavebeenpublishedmainlydifferinthetypesofsmoothingfiltersthatareappliedandthewaythemeasuresofedgestrengtharecomputed.Asmanyedgedetectionmethodsrelyonthecomputationofimagegradients,theyalsodifferinthetypesoffiltersusedforcomputinggradientestimatesinthex-andy-directions.Oncewehavecomputedameasureofedgestrength(typicallythegradientmagnitude),thenextstageistoapplyathreshold,todecidewhetheredgesarepresentornotatanimagepoint.Thelowerthethreshold,themoreedgeswillbedetected,andtheresultwillbeincreasinglysusceptibletonoise,andalsotopickingoutirrelevantfeaturesfromtheimage.Converselyahighthresholdmaymisssubtleedges,orresultinfragmentededges.Iftheedgethresholdingisappliedtojustthegradientmagnitudeimage,theresultingedgeswillingeneralbethickandsometypeofedgethinningpost-processingisnecessary.Foredgesdetectedwithnon-maximumsuppressionhowever,theedgecurvesarethinbydefinitionandtheedgepixelscanbelinkedintoedgepolygonbyanedgelinking(edgetracking)procedure.Onadiscretegrid,thenon-maximumsuppressionstagecanbeimplementedbyestimatingthegradientdirectionusingfirst-orderderivatives,thenroundingoffthegradientdirectiontomultiplesof45degrees,andfinallycomparingthevaluesofthegradientmagnitudeintheestimatedgradientdirection.Acommonlyusedapproachtohandletheproblemofappropriatethresholdsforthresholdingisbyusingthresholdingwithhysteresis.Thismethodusesmultiplethresholdstofindedges.Webeginbyusingtheupperthresholdtofindthestartofanedge.Oncewehaveastartpoint,wethentracethepathoftheedgethroughtheimagepixelbypixel,markinganedgewheneverweareabovethelowerthreshold.Westopmarkingouredgeonlywhenthevaluefallsbelowourlowerthreshold.Thisapproachmakestheassumptionthatedgesarelikelytobeincontinuouscurves,andallowsustofollowafaintsectionofanedgewehavepreviouslyseen,withoutmeaningthateverynoisypixelintheimageismarkeddownasanedge.Still,however,wehavetheproblemofchoosingappropriatethresholdingparameters,andsuitablethresholdingvaluesmayvaryovertheimage.Someedge-detectionoperatorsareinsteadbaseduponsecond-orderderivativesoftheintensity.Thisessentiallycapturestherateofchangeintheintensitygradient.Thus,intheidealcontinuouscase,detectionofzero-crossingsinthesecondderivativecaptureslocalmaximainthegradient.Wecancometoaconclusionthat,tobeclassifiedasameaningfuledgepoint,thetransitioningraylevelassociatedwiththatpointhastobesignificantlystrongerthanthebackgroundatthatpoint.Sincewearedealingwithlocalcomputations,themethodofchoicetodeterminewhetheravalueis“significant”ornotidtouseathreshold.Thuswedefineapointinanimageasbeingasbeinganedgepointifitstwo-dimensionalfirst-orderderivativeisgreaterthanaspecifiedcriterionofconnectednessisbydefinitionanedge.Thetermedgesegmentgenerallyisusediftheedgeisshortinrelationtothedimensionsoftheimage.Akeyprobleminsegmentationistoassembleedgesegmentsintolongeredges.Analternatedefinitionifweelecttousethesecond-derivativeissimplytodefinetheedgeponitsinanimageasthezerocrossingsofitssecondderivative.Thedefinitionofanedgeinthiscaseisthesameasabove.Itisimportanttonotethatthesedefinitionsdonotguaranteesuccessinfindingedgeinanimage.Theysimplygiveusaformalismtolookforthem.First-orderderivativesinanimagearecomputedusingthegradient.Second-orderderivativesareobtainedusingtheLaplacian.數(shù)字圖像處理和邊緣檢測(cè)數(shù)字圖像處理在數(shù)字圖象處理方法的興趣從兩個(gè)主要應(yīng)用領(lǐng)域的莖：改善人類(lèi)解釋圖像信息;和用于存儲(chǔ)，傳輸，和表示用于自主機(jī)器感知圖像數(shù)據(jù)的處理。圖像可以被定義為一個(gè)二維函數(shù)f〔X，Y〕，其中x和y是空間〔平面〕的坐標(biāo)，和f中的任何一對(duì)坐標(biāo)〔X，Y〕的振幅被稱(chēng)為強(qiáng)度或灰度級(jí)在該點(diǎn)的圖像的。當(dāng)x，y和f的振幅值都是有限的，離散的數(shù)量時(shí)，我們稱(chēng)之為圖像的數(shù)字圖像。數(shù)字圖像處理領(lǐng)域是指由數(shù)字計(jì)算機(jī)的裝置處理的數(shù)字圖像。請(qǐng)注意，數(shù)字圖像是由有限數(shù)量的元素，其中每一個(gè)具有特定的位置和值的。這些元件被稱(chēng)作象素，圖像元素，像素和像素。象素是最廣泛地用于表示一個(gè)數(shù)字圖象的元件的術(shù)語(yǔ)。視覺(jué)是最先進(jìn)的我們的感官，所以這并不奇怪，圖像在人類(lèi)感知的一個(gè)最重要的角色。然而，與人類(lèi)不同，誰(shuí)被限定于電磁〔EM〕的可視帶頻譜，成像設(shè)備覆蓋幾乎整個(gè)電磁波譜，從伽馬到無(wú)線(xiàn)電波。他們可以通過(guò)源生成的圖像進(jìn)行操作，人類(lèi)是不習(xí)慣與圖像相關(guān)聯(lián)。這些包括超聲，電子顯微鏡，以及計(jì)算機(jī)生成的圖像。因此，數(shù)字圖像處理包括廣泛的應(yīng)用和不同的領(lǐng)域。有關(guān)于那里的圖像處理站等相關(guān)領(lǐng)域，如圖像分析和計(jì)算機(jī)VI-錫永，啟動(dòng)作者之間沒(méi)有一致的。有時(shí)區(qū)分被定義圖像處理作為一門(mén)學(xué)科，其中輸入和處理的輸出是圖像進(jìn)行。我們認(rèn)為，這是一個(gè)限制的，有點(diǎn)人工邊界。例如，這個(gè)定義下，即使計(jì)算圖像的平均強(qiáng)度〔它產(chǎn)生單號(hào)〕的簡(jiǎn)單的任務(wù)將不被認(rèn)為是一個(gè)圖像處理操作。在另一方面，還有諸如計(jì)算機(jī)視覺(jué)，其最終目標(biāo)是使用計(jì)算機(jī)來(lái)模擬人的視覺(jué)，包括學(xué)習(xí)和能夠作出推斷，并根據(jù)視覺(jué)輸入的操作領(lǐng)域。這個(gè)區(qū)域本身是人工智能〔AI〕，其目的是模仿人類(lèi)智能的一個(gè)分支。AI的領(lǐng)域是其最早在開(kāi)展方面起步階段的階段，已經(jīng)遠(yuǎn)遠(yuǎn)低于原先預(yù)期的進(jìn)展。圖像分析〔也稱(chēng)為圖像理解〕的區(qū)域是在之間的圖像處理和計(jì)算機(jī)視覺(jué)。有一端與計(jì)算機(jī)視覺(jué)的其他在從圖像處理連續(xù)無(wú)皆伐邊界。然而，一個(gè)有用的范例是考慮三種類(lèi)型的計(jì)算機(jī)化過(guò)程中這種連續(xù)：低，中和高一級(jí)的進(jìn)程。低級(jí)別的過(guò)程涉及的原始操作系統(tǒng)蒸發(fā)??散如圖像預(yù)處理，以降低噪聲，比照度增強(qiáng)和圖像銳化。一個(gè)低級(jí)別的方法的特征在于以下事實(shí)既其輸入和輸出都是圖像。上的圖像中級(jí)處理涉及的任務(wù)，例如分割〔分割圖像劃分成多個(gè)區(qū)域或?qū)ο蟆常@些對(duì)象的描述，以減少他們適于計(jì)算機(jī)處理的形式，以及各個(gè)對(duì)象的分類(lèi)〔識(shí)別〕。一個(gè)中層方法的特征在于以下事實(shí)，它的輸入端通常是圖像，但它的輸出是從這些圖像〔例如，邊緣，輪廓，和各個(gè)對(duì)象的身份〕萃取屬性。最后，更高層次的處理涉及“決策意識(shí)”識(shí)別出的對(duì)象的合奏的，如在圖像分析，并且，在連續(xù)體的遠(yuǎn)端，執(zhí)行通常與視覺(jué)有關(guān)的認(rèn)知功能?；谇笆龅脑u(píng)論，我們看到，圖像處理和圖像分析之間的重疊的邏輯位置是識(shí)別單個(gè)區(qū)域或物體的圖像中的區(qū)域。因此，我們要求在這本書(shū)中的數(shù)字圖像處理什么包括其輸入和輸出都圖像，此外，包括從圖像中提取的屬性，直至并包括各個(gè)對(duì)象的識(shí)別過(guò)程的進(jìn)程。作為一個(gè)簡(jiǎn)單的例子，以澄清這些概念，考慮文本的自動(dòng)分析的面積。獲取包含文本的區(qū)域的圖像，預(yù)處理的圖像，提取〔分割〕的單個(gè)字符，描述了適合計(jì)算機(jī)處理的形式特點(diǎn)，并認(rèn)識(shí)到這些單個(gè)字符的過(guò)程是在我們所說(shuō)的數(shù)字范圍在這本書(shū)中的圖像處理。網(wǎng)頁(yè)的內(nèi)容的決策意識(shí)可被視為在圖像分析和甚至計(jì)算機(jī)視覺(jué)的領(lǐng)域之外，取決于復(fù)雜的由語(yǔ)句“決策意識(shí)”。作為隱含的水平不久將變得很明顯，數(shù)字圖像處理，如我們所定義的，是在廣泛的范圍內(nèi)的特殊的社會(huì)和經(jīng)濟(jì)價(jià)值的領(lǐng)域成功地使用。數(shù)字圖像處理的應(yīng)用的領(lǐng)域是如此不同，某種形式的組織是在試圖捕獲該區(qū)域的廣度理想的。一來(lái)開(kāi)發(fā)的圖像處理應(yīng)用的程度的一個(gè)根本的了解的最簡(jiǎn)單的方法是根據(jù)它們的來(lái)源〔例如，視覺(jué)，X-射線(xiàn)，等〕進(jìn)行分類(lèi)的圖像。對(duì)于目前使用的圖像的主要能量源是電磁能量光譜。能量的其他重要來(lái)源包括聲學(xué)，超聲，及電子〔在電子顯微鏡中使用的電子束的形式〕。合成影像，用于建模和可視化，由計(jì)算機(jī)生成的。在本節(jié)中，我們簡(jiǎn)要討論的圖像是如何在這些不同的類(lèi)別和它們的應(yīng)用領(lǐng)域中產(chǎn)生?；趶碾姶挪ㄗV的輻射圖像是在X射線(xiàn)最熟悉的，尤其是圖像和光譜的視覺(jué)頻帶。電磁鐵集成電路波可概念化為傳播不同波長(zhǎng)的正弦波，或者它們可以被認(rèn)為是無(wú)質(zhì)量顆粒的流，以波浪圖案每行駛和以光的速度移動(dòng)。每個(gè)無(wú)質(zhì)量顆粒含有能量的一定量的〔或束〕。能量的每個(gè)束被稱(chēng)為一個(gè)光子。如果譜帶是按照每光子能量分組，我們得到圖2所示的光譜。下面，在一端向另一無(wú)線(xiàn)電波〔最低能量〕從伽馬射線(xiàn)〔最高能量〕。該頻帶被陰影顯示傳達(dá)事實(shí)的電磁波譜的頻帶不清楚而是從一個(gè)到另一個(gè)平滑地過(guò)渡。圖像采集是第一過(guò)程。還要注意被給予一個(gè)形象，已經(jīng)在數(shù)字形式的收購(gòu)可能是簡(jiǎn)單。通常，圖像獲取階段涉及預(yù)處理，如縮放。圖像增強(qiáng)是數(shù)字圖像處理的最簡(jiǎn)單和最吸引人的領(lǐng)域。根本上，后面增強(qiáng)技術(shù)的想法是帶出被遮蔽，或簡(jiǎn)單地以突出的圖像中感興趣的特定特征的細(xì)節(jié)。增強(qiáng)一個(gè)熟悉的例子是，當(dāng)我們?cè)黾?，因?yàn)閳D像的比照度“它看起來(lái)更好?！睘榱擞涀?，增強(qiáng)是圖像處理的一個(gè)非常主觀的領(lǐng)域是很重要的。圖像復(fù)原是也與提高圖像的外觀涉及的區(qū)域。然而，與增強(qiáng)，這是主觀的，圖像恢復(fù)是客觀的，在這個(gè)意義上，恢復(fù)技術(shù)往往是基于圖像退化的數(shù)學(xué)或概率模型。增強(qiáng)，而另一方面，是基于對(duì)什么是“好”的增強(qiáng)效果人的主觀偏好。彩色圖像處理是已經(jīng)在重要性越來(lái)越受到由于在通過(guò)互聯(lián)網(wǎng)的使用數(shù)字圖像的顯著增加的區(qū)域。它涵蓋了在數(shù)字域顏色模型的一些根本概念和根本的色彩處理。顏色也被用于在后面的章節(jié)作為一個(gè)提取圖像的感興趣的特征的根底。小波是代表不同程度分辨率影像的根底。特別是，這種材料是在這本書(shū)的圖像數(shù)據(jù)壓縮和金字塔形表示，在其中圖像被依次細(xì)分成更小的區(qū)域中。壓縮，正如其名稱(chēng)所暗示的，以用于減少保存的圖像，或發(fā)送它。雖然存儲(chǔ)技術(shù)所需的帶寬所需的存儲(chǔ)技術(shù)涉及在過(guò)去十年中顯著提高，同樣不能說(shuō)是為傳輸容量。這是真實(shí)的尤其是在互聯(lián)網(wǎng)上，它的特征是顯著圖畫(huà)內(nèi)容的用途。圖像壓縮是熟悉的〔也許無(wú)意中〕，以在圖像文件的擴(kuò)展，例如以JPEG〔聯(lián)合圖像專(zhuān)家組〕圖像壓縮標(biāo)準(zhǔn)中使用的JPG文件的擴(kuò)展名的形式的計(jì)算機(jī)大多數(shù)用戶(hù)。形態(tài)處理與用于提取在代表性和形狀的描述有用的圖像組件工具交易。本章中的材料開(kāi)始從圖像輸出到流程，輸出圖像屬性工藝過(guò)渡。分割程序分割圖像成其組成局部或?qū)ο?。在一般情況下，自主分割是在數(shù)字圖像處理的最困難的任務(wù)之一。鞏固的分割過(guò)程帶來(lái)的過(guò)程向著要求對(duì)象單獨(dú)確定成像問(wèn)題成功解決一個(gè)很長(zhǎng)的路要走。在另一方面，弱或不穩(wěn)定的分割算法幾乎總能保證最終失敗。在一般情況下，更精確的分割，越容易識(shí)別是成功的。表示和描述幾乎總是跟隨一個(gè)分割階段，這通常是原始像素?cái)?shù)據(jù)的輸出，構(gòu)成任一區(qū)域的邊界對(duì)齊〔即，該組從另一別離一個(gè)圖像區(qū)域中的像素的〕在該區(qū)域或所有的點(diǎn)本身。在這兩種情況下，將數(shù)據(jù)轉(zhuǎn)換為適合于計(jì)算機(jī)處理的形式是必要的。必須作出的第一個(gè)決定是該數(shù)據(jù)是否應(yīng)該被表示為邊界或作為一個(gè)完整的區(qū)域。當(dāng)重點(diǎn)是外部形狀的特征，如角落和語(yǔ)調(diào)邊界表示是適當(dāng)?shù)?。?dāng)重點(diǎn)是內(nèi)部屬性，如紋理或骨骼形狀的區(qū)域表示是適當(dāng)?shù)?。在一些?yīng)用中，這些表示相互補(bǔ)充。選擇的表示是只為反式形成原始數(shù)據(jù)轉(zhuǎn)換成適合于隨后的計(jì)算機(jī)處理的形式的解決方案的一局部。一種方法，也必須用于描述使感興趣的特征被突出顯示數(shù)據(jù)中指定。說(shuō)明，也叫特征選擇，處理與該提取導(dǎo)致一些感興趣的定量信息或者根本從另一個(gè)區(qū)分一個(gè)類(lèi)對(duì)象的屬性。識(shí)別是那一個(gè)標(biāo)簽〔例如，“車(chē)輛”〕分配給根