三维建模技术研究进展(4)

旅游业与现代服务业互动机制研究

　　结束语　实验结果表明,与神经网络和FCM等方法相比较,该算法能够较准确地分割出颅磁共振图像各元素。动态自适应蚁群算法也更具有灵活性,它能灵活地调整聚类过程,可以更准确地识别结果,运行速度比较快,但缺点是一些参数要经过反复的试验才能确定。作为一种新的智能优化计算方法,蚁群算法的许多理论还需要完善,在磁共振图像分割的应用中才刚刚开始,而且也只是局限于实验室阶段。希望随着研究的不断深入,将会有更多的成果出现,并能应用到实际的问题当中。

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徐海祥,喻莉,朱光喜,等.基于支持向量机的磁共振脑组织图像分割[J].中国图像图形学报,2005,10(10):12754～1280

SuriJS,SinghS,puterVisionandPatternRecogni2tionTechniquesfor22Dand32DMRCerebralCorticalSegmention(PartI):AState2of2the2ArtReview[C].PatternAnalysis&Applications,2002,5:46～76

5

参考12

汪红志,聂生东.MR[J]医学工程分册,2005,28～ChiuMing2Jang,Lin2Chih,ChuangKai2Hasiang,etal.Tis2sueSegmentation2AssistedAnalysisofMRIforHumanMotor

Response:AnApproachCombiningArtificialNeuralNetworkandFuzzyCMeans[C].JournalofDigitalImaging,2004,1(5):38～47

DorigoM,BlumC.Antcolonytheory:Asurvey[C].Computer:243～278

62Shuntimeseriesdataforseg2

using[C].EuropeanJour2,173:921～937

7KS.Ahybridapproachforfea2

selectionusingneuralnetworksandantcolonyoptimi2zation[C].ExpertSystemswithApplications,2006,121:134～146

8杨瑞.基于蚁群算法的模糊小波网络控制策略及其应用研究[D]:

[西安理工大学博士论文].2005

9毕晓君.基于智能信息技术的纹理图像识别与生成研究[D]:[哈

尔滨工程大学博士论文].2006

10黄永锋,赵俊,庄天戈.遗传神经网络在颅磁共振图像分割中的应

用[J].上海交通大学学报,2004,38(5):771～774

(上接第210页)

7

DhenainM,RuffinsSW,JacobsRE.Three2dimensionaldigitalmouseatlasusinghighresolutionMRI.DevelopmentalBiology,2001,232(2):458～470

ParkSY,SubbaraoM.Amultiview3Dmodelingsystembasedonstereovisiontechniques.MachineVisionandApplications,2005,16:148～156Y1lmazU,MülayimA,AtalayV.ReconstructionofThreeDi2mensionalModelsfromRealImages.In:ProceedingsoftheFirstInternationalSymposiumon3DDataProcessingVisualizationandTransmission(3DPVT.02),2002

SainzM,PajarolaR,MercadeA.ASimpleApproachforPoint2BasedObjectCapturingandRendering.IEEEComputerGraphicsandApplications,July2August2004.24～33

刘钢,王章野,彭群.自由拍摄视点下的可见外壳生成算法.计算机辅助设计与图形学学报,2004,16(11):1501～1505

MontenegroAA,CarvalhoPCP,VelhoL,GattassM.Spacecarvingwithahand2heldcamera.In:ProceedingsoftheXVIIBrazilianSymposiumonComputerGraphicsandImageProcessing(SIBGRAPI’04),2004.396～403

EstebanCH,puterVisionandImageUnderstanding,2004,96:367～392

SartiA,TubaroS.Imagebasedmultiresolutionimplicitobjectmodeling.EURASIPJ.Appl.SignalProcess,2002,10:1053～1066

BrandM,KangK,CooperDB.Algebraicsolutionforthevisualhull.In:Proceedingsofthe2004IEEEComputerSocietyConfer2enceonComputerVisionandPatternRecognition,CVPR2004.133～135

YangYK,LeeJ,KimSK,KimCH.AdaptiveSpaceCarvingwithTextureMapping.LNCS3482,2005.1129～1138

DeLucaL,VeronP,2puters&Graphics,2006,30:160～176

8

9

10

1112

13

14

15

1617

18DigitalMichelangeloproject.http://graphics.stanford.edu/da2

ta/mich/

19LevoyM,PulliK,CurlessB,etal.ThedigitalMichelangelo

Project:3Dscanningoflargestatues.InSiggraph2000,2000.131～144

20StanforddigitalFormaeUrbisRomaeproject.http://formaur2

bis.stanford.edu/index.html

21MuellerP,VereenoogheT,VergauwenM,VanGoolL,Waelk2

ensM.Photo2realisticanddetailed3Dmodeling:theAntoninenymphaeumatSagalassos(Turkey).ComputerApplicationsandQuantitativeMethodsinArchaeology(CAA):Beyondthearti2fact2Digitalinterpretationofthepast.[http://www.vision.ee.ethz.ch/～pmueller/documents/caa04_pmueller.pdf,accessedMar.2005]

22魏迎梅,栾悉道.虚拟现实技术.电子工业出版社,2005

23LianQin,LiDi2Chen,TangYi2Ping,put2

ermodelingapproachforanovelinternalarchitectureofartificialbone.CADComputerAidedDesign,2006,38(5):507～51424SunW,StarlyB,NamJ,DarlingA.Bio2CADmodelingandits

puter2Ai2dedDesign,2005,37:1097～1114

25SantosDMC,PertenceAEM,CamposHB,CetlinPR.The

developmentof3Dmodelsthroughrapidprototypingconcepts.JournalofMaterialsProcessingTechnology,2005,169:1～426LewisAC,GeltmacherAB.Image2basedmodelingofthere2

sponseofexperimental3Dmicrostructurestomechanicalloading.ScriptaMaterialia,2006,55(1):81～85

27MakkonenT,NevalaK,Heikkil R.A3Dmodelbasedcontrol

ofanexcavator.AutomationinConstruction,2006,15(5):571～577

28QinSF,HarrisonR,WestAA,WrightDK.Developmentofa

putersinIndustry,2004,54:69～81

29邱建雄,赵跃龙,杨瑞元.基于图像的建模和绘制技术综述.小

型微型计算机系统,2004,25(5):908～912

229