图像分割概述.ppt

图像分割概述.ppt

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图像分割图像分割图像识别与人工智能研究所图像识别与人工智能研究所,多谱信息处理国家重点实验室多谱信息处理国家重点实验室陶文兵陶文兵华中科技大学图像识别与人工智能研究所多谱信息处理技术国家重点实验室分割的目的和意义w图像分割是计算机视觉研究中的基础问题和难点之一w图像分割就是把图像分成各具特性的区域并提取出感兴趣目标w图像分割的难点和挑战性n对一般图像中的大量视觉模型进行建模的复杂性n图像理解本身的内在模糊性n当没有一个明确的任务来指导注意机制2图像工程的三层模型imagesegmentationGoal:

BreakuptheimageintomeaningfulorperceptuallysimilarregionsTypesofsegmentationsOversegmentationUndersegmentationMultipleSegmentationsSegmentationasaresultRotheretal.2004SegmentationforefficiencyFelzenszwalbandHuttenlocher2004Hoiemetal.2005,Mori2005ShiandMalik2001SegmentsasprimitivesforrecognitionJ.TigheandS.Lazebnik,submittedtoECCV2010MajorprocessesforsegmentationwBottom-up:

grouptokenswithsimilarfeatureswTop-down:

grouptokensthatlikelybelongtothesameobjectLevinandWeiss2006Bottom-upsegmentationGrouptogethersimilar-lookingpixelsforefficiencyoffurtherprocessingn“Bottom-up”processnUnsupervisedX.RenandJ.Malik.Learningaclassificationmodelforsegmentation.ICCV2003.“superpixels”ThegoalsofsegmentationSeparateimageintocoherent“objects”n“Bottom-up”or“top-down”process?

nSupervisedorunsupervised?

Berkeleysegmentationdatabase:

http:

/www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/imagehumansegmentationTop-downsegmentationwE.BorensteinandS.Ullman,“Class-specific,top-downsegmentation,”ECCV2002wA.LevinandY.Weiss,“LearningtoCombineBottom-UpandTop-DownSegmentation,”ECCV2006.Top-downsegmentationwE.BorensteinandS.Ullman,“Class-specific,top-downsegmentation,”ECCV2002wA.LevinandY.Weiss,“LearningtoCombineBottom-UpandTop-DownSegmentation,”ECCV2006.NormalizedcutsTop-downsegmentation图像分割方法的发展现状w目前图像分割方法主要有三个比较重要的方向：

n基于统计理论的图像分割方法MeanShift，DDMCMCn基于变分模型的图像分割方法SnakeModel，GAC，M-SModel，C-VModeln基于图论的图像分割方法GraphCuts，NormalizeCuts14ThreebasictheoryinImageSegmentationStatisticsVariationalGraphTwobasicModelinImageSegmentationStatisticsformulationVariationalModelEnergyModelsOptimizationMethodBayesianformulationBayesianformulation（GemanandGeman,1984）Geman,S.andD.Geman:

1984,“Stochasticrelaxation,Gibbsdistributions,andtheBayesianrestorationofimages”.IEEETransactionsonPatternAnalysisandMachineIntelligence6,721741.（13641）Dpisadatapenaltyfunction,Vp,qisaninteractivefunctionDatapenaltiesindicateindividuallabel-preferencesofpixelsbasedonobservedintensitiesandprespecifiedlikelihoodfunction.Interactionfunctionencouragespatialcoherencebypenalizingdiscontinuitiesbetweenneighboringpixels.MAP-MRF:

Maximumaposteriori-MarkovrandomfieldSnakemodel（Kassetal.,1988）Kass,M.,A.Witkin,andD.Terzopoulos:

1988,“Snakes:

Activecontourmodels”.InternationalJournalofComputerVision,vol.1,pp.321331（13622）SnakemodelInternalenergyThefirsttwotermscontrolthesmoothnessofthecontourstobedetected.ExternalenergyThethirdtermisresponsibleforattractingthecontourtowardstheobjectintheimage.Geodesicactivecontoursmodel（GAC）（Casellesetal.,1997）V.Caselles,R.Kimmel,andG.Sapiro,“Geodesicactivecontours,”Int.J.Comput.Vis.,vol.22,pp.6179,1997.（3709）Remarks:

Thefunctiongisanedgeindicatorfunctionthatvanishesatobject.Theshorterthecurve,thelargerthegradientofthecurve,thelesstheenergy.Edge-basedActiveContourGeodesicactivecontoursmodelMumfordandShahfunctional（MumfordandShah,1989）Mumford,D.andJ.Shah:

1989,Optimalapproximationsbypiecewisesmoothfunctionsandassociatedvariationalproblems.CommunicationsonPureandAppliedMathematics42,577685.（3122）Mumford-ShahfunctionalRemarks:

TheminimizationofMumford-ShahfunctionalresultsinanoptimalcontourCthatsegmentsthegivenimageu0intoseveralregions.Imageuisanoptimalpiecewisesmoothapproximationofthegivenimageu0ImageuissmoothwithineachoftheconnectedcomponentsintheimagedomainseparatedbythecontourC.CVModel-piecewiseconstantMSmodel（ChanandVese,2001）T.ChanandL.Vese,“Activecontourswithoutedges,”IEEETrans.ImageProcess.,vol.10,no.2,pp.266277,Feb.2001.Citedtimes:

4514Region-basedActiveContourRemarks:

Assumethatuisapiecewiseconstantfunction.Forsuchcase,thesecondtermdisappearsfromtheMSfunctions.Twophaseproblem,c1istheaverageofregion1,c2istheaverageofregion2.Chan-Vese（CV）modelOptimizationMethod1、模拟退火（simulatedannealing）2、水平集算法（LevelSet）3、图割算法（Graphcuts）4、期望最大化算法（Expectation-MaximizationEM）5、置信传播（Beliefpropagation）4、对偶算法（primaldual）