简介:Inthispaper,twoapproachesareusedtosolvethePerspectiveThree-PointProblem(P3P):thesymboliccomputationapproachandthegeometricapproach.Inthesymboliccomputationapproach,weuseWu-Ritt'szerodecompositionalgorithmtogiveacompletetriangulardecompositionfortheP3Pequationsystem.ThisdecompositionprovidesthefirstcompleteanalyticalsolutiontotheP3Pproblem.Inthegeometricapproach,wegivesomepuregeometriccriteriaforthenumberofrealphysicalsolutions.Thecompletesolutionclassificationfortwospecialcaseswiththreeandfourparametersisalsogiven.
简介:AnewmethodforrecognizingChinesecharactersisproposed.Itisbasedontheso-calledfeaturepointsofChinesecharacters.Thefeaturepointsweuseincludethoseonthestrokeofacharacter.i.e.,endpoints.turningpoints,forkpointsandcrosspoints.andthekeypointsonthebackgroundofcharacter.ThismethoddiffersfromthepreviousonesforitcombinesthefeaturepointsonstrokewiththoseonbackgroundanditusesfeaturepointstorecognizeChinesecharactersdirectly.AChinesecharacterrecognitionsystembasedtotop-downdynamicalmatchingoffeaturepointisdeveloped.Thesystemcanrecognizenotonly6763printedsampleSongfontChinesecharactersofsize5.6×5.6mm^2withhighrecognitionrate,butalsothegeneralprintedbooks,magazinesanddocumentswithasatisfactoryrecognitionrateandspeed.
简介:我们在场统一的一个多水平分区为能被设计或在一种含蓄的形式代表的表面重建的代数学的集合表面(MPU-APSS)。代数学的点集合表面(APSS)用本地移动从一套未组织起来的点定义光滑的表面最少平方(MLS)代数学的范围适合。由于本地性质,然而,APSS不为几何学编辑并且当模特儿工作很好。相反,我们的方法基于统一途径的分区为散布的点集合造一个含蓄的近似函数。由使用octree分策略,我们适应地首先为点集合构造本地代数学的范围,然后使用weighting功能一起混合这些本地形状功能。最后,我们从表面计算签署的距离功能的控制错误的近似。另外,我们在场为点使我们的表示合适的一个有效设计操作员设定过滤并且动态点采样。我们为表面重建并且几何学当模特儿例如表面结束表明我们的统一途径的有效性。
简介:Inthispaper,ageometry-basedpointcloudreductionmethodisproposed,andareal-timemobileaugmentedrealitysystemisexploredforapplicationsinurbanenvironments.Weformulateanewobjectivefunctionwhichcombinesthepointreconstructionerrorsandconstraintsonspatialpointdistribution.Basedonthisformulation,amixedintegerprogrammingschemeisutilizedtosolvethepointsreductionproblem.Themobileaugmentedrealitysystemexploredinthispaperiscomposedoftheofttineandonlinestages.Attheofflinestage,webuildupthelocalizationdatabaseusingstructurefrommotionandcompressthepointcloudbytheproposedpointcloudreductionmethod.Whileattheonlinestage,wecomputethecameraposeinrealtimebycombininganimage-basedlocalizationalgorithmandacontinuousposetrackingalgorithm.Experimentalresultsonbenchmarkandrealdatashowthatcomparedwiththeexistingmethods,thisgeometry-basedpointcloudreductionmethodselectsapointcloudsubsetwhichhelpstheimage-basedlocalizationmethodtoachievehighersuccessrate.Also,theexperimentsconductedonamobileplatformshowthatthereducedpointcloudnotonlyreducesthetimeconsumingforinitializationandre-initialization,butalsomakesthememoryfootprintsmall,resultingascalableandreal-timemobileaugmentedrealitysystem.
简介:使用一位口令经理比不使用一个被知道更方便、安全,在口令经理本身是安全的假设上。然而最近的研究证明很流行的口令管理器有没有用户了解,可以被愚弄漏口令的安全危险。在这份报纸,我们建议一个新口令管理器,SplitPass,它垂直地互相把口令的存储和存取分开成二distrusting聚会。在登录期间,聚会将协作送他们的口令的所有分享到这些聚会的网服务者,而是没有曾经将有完全的口令,它显著地提起块聚会损害所有的成功的攻击。为了保留透明性到存在应用程序和网服务器,,SplitPass无缝地切开安全的窝层(SSL)和运输层安全(TCP)会话在所有聚会处理,并且使加入二口令份额透明到网服务器。我们用一个机器人电话和一个云助手实现了SplitPass并且在机器人正式市场从最高的免费应用软件用100个应用软件评估了它。评估证明SplitPass安全地保护用户口令,当几乎不招致很少表演开销和电源消费时。