樹莓大禮包數(shù)字圖像處理課件chapteren

ChapterDigitalImage在最終的分析中,所有知識皆為歷在抽象的意義有科學(xué)皆為數(shù)學(xué)。在理性的世界里,所有判斷皆為統(tǒng)計。 （國立高雄大學(xué)應(yīng)用數(shù)學(xué)系教授Howtopresent andResolutionconcepts(includingspatialandgray ）:readaBMPfile(graylevelimage)fromharddrive,dis yitonthescreenandrewriteitintoBMPfilebacktoharddrive.Reloadtheimageagainanddothedown-sampling(reducethespecialresolution)and zation(reducegraylevelVisualLightandtheElectromagneticImageSensingandImageSampling BasicgeometricrelationshipsbetweenLinearandNonlinear2.1ElementsofVisualVisual——Key人的視覺是由眼睛中兩部分 （感覺細胞）組成的：錐狀（cones）和桿狀體(rods)每只眼睛錐狀體數(shù)目6-7對色彩敏感，分布于視網(wǎng) 凹部分，稱為白晝視覺或亮光視覺桿狀體 萬到 膜面幾桿體接到錐體給圖的體沒彩感，感稱夜覺暗。mm×1mm5mm×5mmCC:Chr-cdc片成像原理：和光學(xué)透鏡類似，但適應(yīng)性強，是可自行調(diào)節(jié)的透鏡StructureofhumanStructureofhuman2.1ElementsofVisualDistributionofConesandRodsonRodsarehighlyresponsivetolight,butseeonlyabandoflight,andthereforecannotdiscriminateResponsibleforperceptionunderlow-intensityLowspatialHighersensitivitytotemporalLesssensitivetoConesarelessresponsive,butseethreedistinctcolorbandsoflight,enablingcolorFoveaVision:ResponsibleforperceptionunderhighintensityVeryhighspatialresolution(near1min.visualLesssensitivetotemporalHighlysensitivetoLightDetection:Rodsand-120millionrodsin-1000XmorelightsensitivethanDiscriminateB/WbrightnessinlowShortwave-length6-7millionConesintheResponsibleforhigh-resolutionDiscriminateThreetypesofcolorsensors(64%red,32%,2%SensitivetoanycombinationofthreeImageformationinthe

ElementsofVisual成像原理：和光學(xué)透鏡類似，但適應(yīng)性強，是可自行調(diào)節(jié)的透鏡看遠處物體，肌肉會迫使晶狀體變得扁平，晶狀體的聚焦中心距由向變化，晶狀體的折射能力也由弱變強。當(dāng)物體距離過的原因之一Imageformationinthe

ElementsofVisualThefocallengthoftheeyevariousfrom14mmtoImageformationinthe

ElementsofVisualThefocallengthoftheeyevariousfrom14mmtoImageformationinthe

ElementsofVisualThefocallengthoftheeyevariousfrom14mmto2.1ElementsofVisual兩個現(xiàn)象說明了亮度感覺兩個現(xiàn)象說明了亮度感覺不是簡單的強度函第一個現(xiàn)象是視覺系統(tǒng)傾向于不同強度區(qū)域邊界周圍的（Undershoot）和“過調(diào)ThetruechangeinintensityThetruechangeinintensityisastepfunction,asshownbysolidline.Humaneyetendstoincreasethecontrastattheedgeoftwostepsbyundershootingandovershooting,asindicatedbydashline.Thisphenomenaiscalledthe‘Machbands’(1865).ElementsofVisual第二個現(xiàn)象稱為同時對比現(xiàn)象(SimultaneousElementsofVisual視覺錯覺：這是人類視覺系統(tǒng)的一個特性，尚未完全了EbbinghausEbbinghaus6.16.1ColorForForGrandTetonGrandTeton2.22.2LightandElectromagneticLightandElectromagneticSirIsaacNewton1666discovered:whenabeamofsunlightispassedthroughaglassprism,theemergingbeamoflightisnotwhiteagainbutcontainsacontinuousspectrumofcolorsrangingfromvioletatoneendtoredattheother.2.22.2LightandElectromagnetic（1nm=10-9m）的范圍。波譜一端是無線電波，波長比可見光長幾十億倍；另LightandElectromagnetic為：電磁波可以用波長、頻率或能量來描述。波長(λ)和頻率(ν)關(guān)系 其中c是光速（2.998x108m/s），電磁波譜(的分量由下式給h是普克朗常數(shù)。能量與頻率成正電磁波可以看成是以波長 的正弦波關(guān)于可見光的一些基本事

LightandElectromagnetic可見光是一種特殊的電磁波譜，只在電磁波譜中占很小的一部眼睛從物體上感受到的顏色和物體發(fā)射光的性質(zhì)有關(guān)。一個物體對有可見光波長的反射是相對平衡的，則這個物體將呈現(xiàn)白色（灰色）單色光的屬性是它的強度或大小?；叶燃壨ǔＪ怯脕砻枥L單色光強度，它的范圍從黑到灰，最后到白通常有三個基本量用于描繪彩色光源的質(zhì)量：發(fā)光強度（出的能量）、光通量（觀察者從光源感受的能量，例如：遠紅外光有實）描繪，它實際上不能測量，包含無色的強度的概念，并且也是描述彩色感覺的參波長和被“觀察”物體的大小之間的關(guān)ImageSensingandImagesensingandLinecamera(不要求掌握Linecameraisusedwhentheobjectmoves(rotate)theotherdirectiontocompletea2-D不要求掌

2.3ImageSensingand不要求掌2.3ImageSensingand2.3ImageSensingandSchematicdiagramshowingimagecapturing實整簡單的圖像模

2.3ImageSensingand用二維函數(shù)的形式表示一幅 像。當(dāng)一幅圖像從物理過程產(chǎn)生時它的值正比于物理源的輻射能量（如電磁波）。故一定另外，函數(shù)?(x,y)有兩個分量來表征：入射到觀察場景的光源總量場景中物體反射光的總量。稱為入射分量和反射分量，并分別i(x,y)和r(x,y)表示。兩個函數(shù)合并形成圖像函數(shù)?(x,y)：?(x,y)=i(x,y) r(x,y)其 像上任一點的強度就是圖像在那一點的灰度級。反射分量限制在（全反射）和1（全吸收）之間2.4ImageSampling ImageSampling （圖像圖像，需要把連續(xù)的感知數(shù)據(jù)轉(zhuǎn)換為數(shù)字形式。這就包含了兩種處理，取和量化（光色強度等ConceptsofImageSampling before)。對應(yīng)于空間分辨率：分辨率2.4ImageSampling ImageSampling Sampling zedusing8-bitsgreyTheimageissampled(withfixedspatialresolution/frequency) zedinto8-bitsgreylevel,thenthegreylevel(oftheimage)isrecordedasamatrixwiththecoordinatesystemshownontheupperright.2.4ImageSampling Sampledimagecanberepresentedbyatwo-Df(x,y)

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2.4ImageSampling 出于處理 和硬件的考慮，灰度級別通常是2的整數(shù)L是最大的灰度級別。這時，圖像中所有像素的灰度是區(qū) [0，L- 整數(shù)。一幅數(shù)字圖像占用的空間：M×N×k（？）2.4ImageSampling SpatialandGray-LevelDownSamplingdownfrom1024×1024toHowdowedo2.4ImageSampling SpatialandGray-LevelDown1

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2.4ImageSampling AliasingandMoiréAliasing:highfrequencypatternmis-interpretedaslowfrequencypattern,itoccurwhentheimageissampledattoolowafrequency.Toavoidaliasing,theNyquistsamplingtheoremhastobesamplingfrequency?S≥ umfrequencyintheMoirépatternoccurwhentwoidenticalperiodicpatternoverlappedwithanintersectionangleasshown Properlyimageofbrick

SpatialaliasingintheformofaMoirépatternReallyBadClickReallyBadReallyBadZooming(up-sampling)adigitalZooming(up-sampling)bynearestneighborZooming(up-sampling)bybilinear2.4ImageSampling Intensitygreylevelresolution強(灰)度分辨率2.4ImageSampling 2.4ImageSampling 人眼對灰度分辨率的敏感程度和圖像內(nèi)容的復(fù)雜程度相2.4ImageSampling 2.52.5SomeBasicRelationshipsBetweenBasicspatialrelationshipsbetween相相鄰像位于坐標(x,y)的像素p有四個水平和垂直鄰像素，每個像素距(x,y)一個單位距離

(x, 像素p的4鄰域，用N4(p)表示另外，p有4個對角相鄰像素(x-1,y-1),(x+1,y-1),(x-1,y+1),(x+1,用ND(p)表示，和N4(P)一起稱為P的8N8(P)表

Neighbors（相鄰點）：主要有兩種定義：4-neighbors和8-4-neighbors定義較嚴：一個點（色）周邊有四個相鄰點（藍色8-neighbors定義較松：一個點（色）周邊有八個相鄰點（藍色4-neighbors一定是8-neighbors，反之不一定Path（路徑）：也有4-path和8-4-path定義較嚴：一個路徑上所點互4-8-path定義較松：一個路徑上所點互8-在4-path定義下，藍色定定定定2.52.5SomeBasicRelationshipsBetween鄰接性有兩個要素：一個是灰度值的鄰接性（值域V）、一個是物位置的鄰接性（鄰域，如N4(P)等）。例如，二值圖象中，像素值都為（或都為0）的像素才有可能被稱為是鄰接的。在一般圖像中，可定義個值域是0到255中的任一個子一一般我們考慮三種鄰接4鄰接：如果點在N4(P)中，數(shù)值在中，則和是4鄰接8鄰接：如果點在N8(P)中，數(shù)值在中，則和是8鄰接的（c）m鄰接（混合鄰接）：滿足下列條件的任一個，則具有的和是連接的在中2.52.5SomeBasicRelationshipsBetween注意：混合鄰接是8鄰接的改進，為了消除8鄰接的二義性。例如2.26中的某些像素和或者。距距離度量（見書本TheD4distance(alsocalledthe‘cityblockTheD8distance(alsocalledthe‘chessboard2.6MathematicaltoolsusedinAnAnintroductiontotheMathematicalToolsUsedinArrayversusMatrix

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a22b22MatrixMatrixaaabaaab21 21 ba21 21 2.6Mathematicaltoolsusedin2.6.2.2.6.2.LinearversusNonlinearH——anoperator,bothfandgareimages.ALinearoperatorhassatisfythefollowingrules(aandbarescalarH(afbg)aH(f)bH(g)Examples:Examples:sumoperator,Σ;max2.6Mathematicaltoolsusedin2.6.3.2.6.3.Arithmeticd(x,y)=p(x,y)=f(x,y)v(x,y)=f(x,y)2.6MathematicaltoolsusedinAveragingKimagestoremovezero-mean2.6MathematicaltoolsusedinAveragingKimagestoremovezero-mean2.6MathematicaltoolsusedinImage

2.6MathematicaltoolsusedinImage

2.6Mathematicaltoolsusedin 2.6MathematicaltoolsusedinImagemultiplication(and2.6Mathematicaltoolsusedin2.6.4.2.6.4.SetandLogicalBasicset2.6Mathematicaltoolsusedin2.6.4.2.6.4.SetandLogicalExample2.8:AgrayimageAcanberepresentedby(x,y,z),wherezdenotesintensity.Ac={(x,y,K-z)},Kisaconstant(suchasK=28-1).2.6Mathematicaltools2.6Mathematicaltoolsusedin2.62.6MathematicaltoolsusedinFuzzyset模糊集SpatialSingle-pixeloperations:onepixelintheoriginalimageproduceonepixelintheoutputimage2.6Mathematical2.6Mathematicaltoolsusedin2.6MathematicaltoolsusedinGeometricspatialtransformationsandimageGeometrictr