數(shù)字圖像處理：圖象分割1026

第七章．圖像分割（ImageSegmentation）圖象采集圖象預(yù)處理圖象分割特征提取與描述－１特征選擇模式識別－－２特征參數(shù)測量一．并行邊界技術(shù)二．串行邊界技術(shù)三．并行區(qū)域技術(shù)四．串行區(qū)域技術(shù)五．分割性能評價圖象分割：把圖象分成各具特性的區(qū)域，并提取感興趣目標(biāo)的技術(shù)和過程．這里的特性主要指的是：灰度，顏色，紋理或運動等．其一是通過直接確定區(qū)域間的邊界來實現(xiàn)分割的邊界方法；其二是將各像素劃歸到相應(yīng)物體或區(qū)域的像素聚類方法,即區(qū)域法；圖像中不同類型的邊界（a）邊界；（b）線；（c）折線變化；（d）緩慢的平滑變化(a)(b)(d)(c)FirstandSecondderivativesthesignsofthederivativeswouldbereversedforanedgethattransitionsfromlighttodarkEdgeDetectionthemostcommonapproachfordetectingmeaningfuldiscontinuitiesingraylevel.wediscussapproachesforimplementingfirst-orderderivative(Gradientoperator)second-orderderivative(Laplacianoperator)Here,wewilltalkonlyabouttheirpropertiesforedgedetection.ImageSegmentationImageSegmentationLaplacian(linearoperator)LaplacianoperatorImageSegmentationImageSegmentationGradientOperatorfirstderivativesareimplementedusingthemagnitudeofthegradient.themagnitudebecomesnonlinearcommonlyapprox.GradientdirectionLet(x,y)representthedirectionangleofthevectorfat(x,y)(x,y)=tan-1(Gy/Gx)Thedirectionofanedgeat(x,y)isperpendiculartothedirectionofthegradientvectoratthatpointCriteriathestrengthoftheresponseofthegradientoperatorusedtoproducetheedgepixelanedgepixelwithcoordinates(x0,y0)inapredefinedneighborhoodof(x,y)issimilarinmagnitudetothepixelat(x,y)if|f(x,y)-f(x0,y0)|ECriteriathedirectionofthegradientvectoranedgepixelwithcoordinates(x0,y0)inapredefinedneighborhoodof(x,y)issimilarinangletothepixelat(x,y)if|(x,y)-(x0,y0)|<ACriteriaApointinthepredefinedneighborhoodof(x,y)islinkedtothepixelat(x,y)ifbothmagnitudeanddirectioncriteriaaresatified.theprocessisrepeatedateverylocationintheimagearecordmustbekeptsimplybyassigningadifferentgrayleveltoeachsetoflinkededgepixels.Examplefindrectangleswhosesizesmakesthemsuitablecandidatesforlicenseplates

usehorizontalandverticalSobeloperatorslinkconditions:gradientvalue>25gradientdirectiondiffers<15

eliminateisolatedshortsegmentsHoughTransformation(Line)yi=axi+bb=-axi+yiab-planeorparameterspacexy-planeallpoints(xi,yi)containedonthesamelinemusthavelinesinparameterspacethatintersectat(a’,b’)Accumulatorcells(amax,amin)and(bmax,bmin)aretheexpectedrangesofslopeandinterceptvalues.allareinitializedtozeroifachoiceofapresultsinsolutionbqthenwelet

A(p,q)=A(p,q)+1attheendoftheprocedure,valueQinA(i,j)correspondstoQpointsinthexy-planelyingontheliney=aix+bjb=-axi+yi-planeproblemofusingequationy=ax+bisthatvalueofaisinfiniteforaverticalline.Toavoidtheproblem,useequationxcos+ysin=torepresentalineinstead.verticallinehas=90withequalstothepositivey-interceptor=-90withequalstothenegativey-interceptxcos+ysin=-plane

=90measuredwithrespecttox-axiswhereDisthedistancebetweencornersintheimageGeneralizedHoughTransformationcanbeusedforanyfunctionoftheformg(v,c)=0visavectorofcoordinatescisavectorofcoefficientsHoughTransformation(Circle)equation:(x-c1)2+(y-c2)2=c32threeparameters(c1,c2,c3)cubelikecellsaccumulatorsoftheformA(i,j,k)incrementc1andc2,solveofc3thatsatisfiestheequationupdatetheaccumulatorcorrespondingtothecellassociatedwithtriplet(c1,c2,c3)Edge-linkingbasedonHoughTransformationComputethegradientofanimageandthresholdittoobtainabinaryimage.Specifysubdivisionsinthe-plane.Examinethecountsoftheaccumulatorcellsforhighpixelconcentrations.Examinetherelationship(principallyforcontinuity)betweenpixelsinachosencell.Thresholdingimagewithdarkbackgroundand

alightobjectimagewithdarkbackgroundand

twolightobjectsBasicGlobalThresholdinggeneratebinaryimageuseTmidwaybetweenthemaxandmingraylevelsBasicGlobalThresholdingbasedonvisualinspectionofhistogramSelectaninitialestimateforT.SegmenttheimageusingT.Thiswillproducetwogroupsofpixels:G1consistingofallpixelswithgraylevelvalues>TandG2consistingofpixelswithgraylevelvaluesTComputetheaveragegraylevelvalues1and2forthepixelsinregionsG1andG2ComputeanewthresholdvalueT=0.5(1+2)Repeatsteps2through4untilthedifferenceinTinsuccessiveiterationsissmallerthanapredefinedparameterTo.Example:Heuristicmethodnote:theclearvalleyofthehistogramandtheeffectiveofthesegmentationbetweenobjectandbackgroundT0=03iterationswithresultT=125Example:AdaptiveThresholdingOptimalGlobalandAdaptiveThresholdingProbabilityoferroneouslyMinimumerrorDifferentiatingE(T)withrespecttoT(usingLeibniz’srule)andequatingtheresultto0findTwhichmakesifP1=P2thentheoptimumthresholdiswherethecurvep1(z)andp2(z)intersectGaussiandensityExample:usePDF=Gaussiandensity:p1(z)andp2(z)where1and12arethemeanandvarianceoftheGaussiandensityofoneobject2and22arethemeanandvarianceoftheGaussiandensityoftheotherobjectQuadraticequationifP1=P2or=0thentheoptimalthresholdistheaverageofthemeansRegion-BasedSegmentationBasicFormulationP(Ri)isalogicalpredicatepropertydefinedoverthepointsinsetRiex.P(Ri)=TRUEifallpixelinRihavethesamegraylevelRegionGrowingstartwithasetof“seed”pointsgrowingbyappendingtoeachseedthoseneighborsthathavesimilarpropertiessuchasspecificrangesofgraylevelRegionGrowingselectallseedpointswithgraylevel255criteria:theabsolutegray-leveldifferencebetweenanypixelandtheseedhastobelessthan65thepixelhastobe8-connectedtoatleastonepixelinthatregion(ifmore,theregionsaremerged)RegionsplittingandmergingQuadtreeSplitinto4disjointquadrantsanyregionRiforwhich

P(Ri)=FALSEMergeanyadjacentregionRjandRkforwhich

P(Ri

Rk)=TRUEStopwhennofurthermergingorsplittingispossible.