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數(shù)字圖像處理在數(shù)字圖象處理方法的興趣從兩個主要應(yīng)用領(lǐng)域的莖：改善人類解釋圖像信息;和用于存儲，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中的任何一對坐標(biāo)〔X，Y〕的振幅被稱為強(qiáng)度或灰度級在該點的圖像的。當(dāng)x，y和f的振幅值都是有限的，離散的數(shù)量時，我們稱之為圖像的數(shù)字圖像。數(shù)字圖像處理領(lǐng)域是指由數(shù)字計算機(jī)的裝置處理的數(shù)字圖像。請注意，數(shù)字圖像是由有限數(shù)量的元素，其中每一個具有特定的位置和值的。這些元件被稱作象素，圖像元素，像素和像素。象素是最廣泛地用于表示一個數(shù)字圖象的元件的術(shù)語。視覺是最先進(jìn)的我們的感官，所以這并不奇怪，圖像在人類感知的一個最重要的角色。然而，與人類不同，誰被限定于電磁〔EM〕的可視帶頻譜，成像設(shè)備覆蓋幾乎整個電磁波譜，從伽馬到無線電波。他們可以通過源生成的圖像進(jìn)行操作，人類是不習(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，如圖像分析和計算機(jī)VI-錫永，啟動作者之間沒有一致的。有時區(qū)分被定義圖像處理作為一門學(xué)科，其中輸入和處理的輸出是圖像進(jìn)行。我們認(rèn)為，這是一個限制的，有點人工邊界。例如，這個定義下，即使計算圖像的平均強(qiáng)度〔它產(chǎn)生單號〕的簡單的任務(wù)將不被認(rèn)為是一個圖像處理操作。在另一方面，還有諸如計算機(jī)視覺，其最終目標(biāo)是使用計算機(jī)來模擬人的視覺，包括學(xué)習(xí)和能夠作出推斷，并根據(jù)視覺輸入的操作領(lǐng)域。這個區(qū)域本身是人工智能〔AI〕，其目的是模仿人類智能的一個分支。AI的領(lǐng)域是其最早在開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〔也稱為圖像理解〕的區(qū)域是在之間的圖像處理和計算機(jī)視覺。有一端與計算機(jī)視覺的其他在從圖像處理連續(xù)無皆伐邊界。然而，一個有用的范例是考慮三種類型的計算機(jī)化過程中這種連續(xù)：低，中和高一級的進(jìn)程。低級別的過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。一個低級別的方法的特征在于以下事實既其輸入和輸出都是圖像。上的圖像中級處理涉及的任務(wù)，例如分割〔分割圖像劃分成多個區(qū)域或?qū)ο蟆常@些對象的描述，以減少他們適于計算機(jī)處理的形式，以及各個對象的分類〔識別〕。一個中層方法的特征在于以下事實，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，和各個對象的身份〕萃取屬性。最后，更高層次的處理涉及“決策意識”識別出的對象的合奏的，如在圖像分析，并且，在連續(xù)體的遠(yuǎn)端，執(zhí)行通常與視覺有關(guān)的認(rèn)知功能。基于前述的評論，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別單個區(qū)域或物體的圖像中的區(qū)域。因此，我們要求在這本書中的數(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，直至并包括各個對象的識別過程的進(jìn)程。作為一個簡單的例子，以澄清這些概念，考慮文本的自動分析的面積。獲取包含文本的區(qū)域的圖像，預(yù)處理的圖像，提取〔分割〕的單個字符，描述了適合計算機(jī)處理的形式特點，并認(rèn)識到這些單個字符的過程是在我們所說的數(shù)字范圍在這本書中的圖像處理。網(wǎng)頁的內(nèi)容的決策意識可被視為在圖像分析和甚至計算機(jī)視覺的領(lǐng)域之外，取決于復(fù)雜的由語句“決策意識”。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)的特殊的社會和經(jīng)濟(jì)價值的領(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。一來開發(fā)的圖像處理應(yīng)用的程度的一個根本的了解的最簡單的方法是根據(jù)它們的來源〔例如，視覺，X-射線，等〕進(jìn)行分類的圖像。對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，由計算機(jī)生成的。在本節(jié)中，我們簡要討論的圖像是如何在這些不同的類別和它們的應(yīng)用領(lǐng)域中產(chǎn)生。基于從電磁波譜的輻射圖像是在X射線最熟悉的，尤其是圖像和光譜的視覺頻帶。電磁鐵集成電路波可概念化為傳播不同波長的正弦波，或者它們可以被認(rèn)為是無質(zhì)量顆粒的流，以波浪圖案每行駛和以光的速度移動。每個無質(zhì)量顆粒含有能量的一定量的〔或束〕。能量的每個束被稱為一個光子。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，在一端向另一無線電波〔最低能量〕從伽馬射線〔最高能量〕。該頻帶被陰影顯示傳達(dá)事實的電磁波譜的頻帶不清楚而是從一個到另一個平滑地過渡。圖像采集是第一過程。還要注意被給予一個形象，已經(jīng)在數(shù)字形式的收購可能是簡單。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)一個熟悉的例子是，當(dāng)我們增加，因為圖像的比照度“它看起來更好。”為了記住，增強(qiáng)是圖像處理的一個非常主觀的領(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)在重要性越來越受到由于在通過互聯(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，正如其名稱所暗示的，以用于減少保存的圖像，或發(fā)送它。雖然存儲技術(shù)所需的帶寬所需的存儲技術(shù)涉及在過去十年中顯著提高，同樣不能說是為傳輸容量。這是真實的尤其是在互聯(lián)網(wǎng)上，它的特征是顯著圖畫內(nèi)容的用途。圖像壓縮是熟悉的〔也許無意中〕，以在圖像文件的擴(kuò)展，例如以JPEG〔聯(lián)合圖像專家組〕圖像壓縮標(biāo)準(zhǔn)中使用的JPG文件的擴(kuò)展名的形式的計算機(jī)大多數(shù)用戶。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。本章中的材料開始從圖像輸出到流程，輸出圖像屬性工藝過渡。分割程序分割圖像成其組成局部或?qū)ο蟆Ｔ谝话闱闆r下，自主分割是在數(shù)字圖像處理的最困難的任務(wù)之一。鞏固的分割過程帶來的過程向著要求對象單獨確定成像問題成功解決一個很長的路要走。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，越容易識別是成功的。表示和描述幾乎總是跟隨一個分割階段，這通常是原始像素數(shù)據(jù)的輸出，構(gòu)成任一區(qū)域的邊界對齊〔即，該組從另一別離一個圖像區(qū)域中的像素的〕在該區(qū)域或所有的點本身。在這兩種情況下，將數(shù)據(jù)轉(zhuǎn)換為適合于計算機(jī)處理的形式是必要的。必須作出的第一個決定是該數(shù)據(jù)是否應(yīng)該被表示為邊界或作為一個完整的區(qū)域。當(dāng)重點是外部形狀的特征，如角落和語調(diào)邊界表示是適當(dāng)?shù)摹．?dāng)重點是內(nèi)部屬性，如紋理或骨骼形狀的區(qū)域表示是適當(dāng)?shù)摹Ｔ谝恍?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區(qū)分一個類對象的屬性。識別是那一個標(biāo)簽〔例如，“車輛”〕分配給根