预览加载中，请您耐心等待几秒...

基于标准割(Normalizedcut)算法图像分割方法.docx

预览
1/2
2/2

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

基于标准割(Normalizedcut)算法图像分割方法 Introduction Imagesegmentationisakeytechniqueinthefieldofimageprocessing,whichplaysanimportantroleinthefieldofcomputervision,imagerecognition,andpatternrecognition.NormalizedCutalgorithmisawidelyusedmethodforimagesegmentation,whichcanseparateanimageintomeaningfulclustersbasedonthesimilarityofitsrespectiveregions.Inthispaper,wewillintroducethebasicprinciplesandapplicationofNormalizedCutalgorithminimagesegmentation,aswellasitsadvantagesandlimitations. PrinciplesofNormalizedCutAlgorithm NormalizedCutalgorithmisagraph-basedmethod,whichpartitionstheimageintodifferentregionsbasedonthesimilaritymeasure.ThebasicprincipleoftheNormalizedCutalgorithmistominimizethe“cut”betweendifferentregionswhilemaximizingthe“similarity”withineachregion.Theimagecanberepresentedasagraph,wherethepixelsaretheverticesandthelinksrepresentthesimilaritybetweenpixels.ThegraphcanberepresentedasW=[wij]∈Rn×n,wherewijrepresentsthesimilaritybetweenpixeliandpixelj.TheNormalizedCutalgorithmpartitionsthegraphintotwodisjointsubsetsAandB,whicharethetwopartitionsoftheimage,minimizingthecutbetweenthem. ThecutbetweenAandBisdefinedasthesumoftheweightsofallthelinksbetweenthetwosubsets,whichcanbeexpressedasCut(A,B)=∑i∈A,j∈BWij.Thesimilaritywithinthepartitionisdefinedbythesumofalltheweightsofthelinkswithineachsubset,whichcanbeexpressedasVol(A)=∑i∈AWii.TheNormalizedCutalgorithmcalculatesthecostfunctionas: NCut(A,B)=Cut(A,B)/Vol(A)+Cut(A,B)/Vol(B) ThealgorithmiterativelypartitionsthegraphintotwosubsetsAandBbyminimizingtheNCutcostfunction.TheoptimalpartitionisachievedwhentheNCutfunctionisminimized.TheNormalizedCutalgorithmgeneratesabinarymask,wherethepixelsareassignedtoeitherpartitionAorpartitionB. ApplicationsofNormalizedCutAlgorithm NormalizedCutalgorithmhasawiderangeofapplicationsinimagesegmentation.OneofthemajorapplicationsofNormalizedCutalgorithmisinimagesegmentationofmedicalimages,suchasMRI,CT,andX-rayimages.Imagesegmentationisessentialinthediagnosisandtreatmentofvariousmedicalconditio