正在加载图片...
,216. 北京科技大学学报 第30卷 [6]Wu QZ,Jeng B S.Background subtraction based on logarithmic [11]Nicolas H.Pinel J M.Joint moving cast shadows segmentation intensities.Pattern Recognit Lett.2002.23(13):1529 and light source detection in video sequences.Signal Process [7]Stauffer C,Grimson W E L.Adaptive background mixture mod- Image Commun.2006.21(1):22 els for real-time tracking.Proc IEEE Comput Soc Conf Comput [12]Salvador E,Cavallaro A,Ebrahimi T.Cast shadow segmenta- Vision Pattern Recognit.1999.2:246 tion using invariant color features.Comput Vision Image Un- [8]Zivkovic Z.Improved adaptive Gaussian mixture model for back- derstanding.2004.95(2):238 ground subtraction//Proceedings of the 17th International Con- [13]Guan Y P.Gu W K.Automatic and robust shadow segmenta- ference on Pattern Recognition.2004.2:28 tion from two-dimensional scenes.Chin J Electron,2006.34 [9]Ridder C.Munkelt O.Kirchner H.Adaptive background estima- (4):624 tion and foreground detection using Kalman filtering.Proc Int (管业鹏，顾伟康。二维场景阴影区域的自动鲁棒分制.电子 Conf Recent Adu Mechatron,1995,1:193 学报，2006,34(4)：624) [10]Leone A.Distante C.Buccolieri F.A shadow elimination ap- [14]Zhang Y J.Image Segmentation.Beijing:Science Press. proach in video"surveillance context.Pattern Recognit Lett, 2001.88 2006,27(5):345 (章毓晋.图像分割，北京：科学出版社，2001：88) (上接第211页) (阳建宏，徐金梧，杨德斌，等.基于相重构和主流形识别的 非线性时间序列降噪方法.北京科技大学学报，2005,27(5)： 631) 参考文献 [4]Takens F.Lecture Notes in Math.New York:Springer.1981: 366 [1]Ren R.Xu J,Zhu S H.Prediction of chaotic time sequence using least squares support vector domain.Acta Phys Sin,2006.55 [5]Cao L Y,Mees A.Judd K.Dynamics from multivariate time se- (2):555 ies.PhD.1998,121,75 [6]Sauer T,Yorke J A,Casdagli M.Embedology.J Stat Phys, (任韧，徐进，朱世华.最小二乘支持向量域的混沌时间序列 1991,65:579 预测.物理学报，2006,55(2)：555) [2]Meng Q F.Zhang Q.Mu W Y.A novel multi-step adaptive pre- [7]Porporato A,Ridolfi L.Multivariate nonlinear prediction of river flows.J Hydrol.2001.248:109 diction method for chaotic time series.Acta Phys Sin.2006.55 (4):1666 [8]Alparslan A K.Sayar M.Atilgan A R.State-space prediction (孟庆芳，张强，牟文英，混沌时间序列多步自适应预测方法， model for chaotic time series.Phys Rev E.1998.58(2):2640 物理学报，2006,55(4)：1666) [9]Jaditz T,Riddick A.Time-series near neighbor regression.Stud [3]Yang J H.Xu J W,Yang D B.et al.Nonlinear time series noise Nonlinear Dyn Econometr.2000.4(1):35 reduction method based on phase reconstruction and principal [10]Grassberger P,Procaccia I.Characterization of Strange Attrac- tors.Phys Rev Lett.1983.50(5):346 manifold learning-JUnie Sci Technol Beijing.2005.27(5):631［6］ Wu Q ZJeng B S．Background subtraction based on logarithmic intensities．Pattern Recognit Lett200223（13）：1529 ［7］ Stauffer CGrimson W E L．Adaptive background mixture models for rea-l time tracking．Proc IEEE Comput Soc Conf Comput V ision Pattern Recognit19992：246 ［8］ Zivkovic Z．Improved adaptive Gaussian mixture model for background subtraction∥ Proceedings of the17th International Conference on Pattern Recognition20042：28 ［9］ Ridder CMunkelt OKirchner H．Adaptive background estimation and foreground detection using Kalman filtering．Proc Int Conf Recent A dv Mechatron19951：193 ［10］ Leone ADistante CBuccolieri F．A shadow elimination approach in video-surveillance context． Pattern Recognit Lett 200627（5）：345 ［11］ Nicolas HPinel J M．Joint moving cast shadows segmentation and light source detection in video sequences． Signal Process Image Commun200621（1）：22 ［12］ Salvador ECavallaro AEbrahimi T．Cast shadow segmentation using invariant color features．Comput V ision Image Understanding200495（2）：238 ［13］ Guan Y PGu W K．Automatic and robust shadow segmentation from two-dimensional scenes．Chin J Electron200634 （4）：624 （管业鹏顾伟康．二维场景阴影区域的自动鲁棒分割．电子 学报200634（4）：624） ［14］ Zhang Y J． Image Segmentation．Beijing：Science Press 2001：88 （章毓晋．图像分割．北京：科学出版社2001：88） （上接第211页） 参 考 文 献 ［1］ Ren RXu JZhu S H．Prediction of chaotic time sequence using least squares support vector domain． Acta Phys Sin200655 （2）：555 （任韧徐进朱世华．最小二乘支持向量域的混沌时间序列 预测．物理学报200655（2）：555） ［2］ Meng Q FZhang QMu W Y．A novel mult-i step adaptive prediction method for chaotic time series．Acta Phys Sin200655 （4）：1666 （孟庆芳张强牟文英．混沌时间序列多步自适应预测方法． 物理学报200655（4）：1666） ［3］ Yang J HXu J WYang D Bet al．Nonlinear time series noise reduction method based on phase reconstruction and principal manifold learning．J Univ Sci Technol Beijing200527（5）：631 （阳建宏徐金梧杨德斌等．基于相重构和主流形识别的 非线性时间序列降噪方法．北京科技大学学报200527（5）： 631） ［4］ Takens F．Lecture Notes in Math．New York：Springer1981： 366 ［5］ Cao L YMees AJudd K．Dynamics from multivariate time series．Phys D．1998121：75 ［6］ Sauer TYorke J ACasdagli M．Embedology．J Stat Phys 199165：579 ［7］ Porporato ARidolfi L．Multivariate nonlinear prediction of river flows．J Hydrol2001248：109 ［8］ Alparslan A KSayar MAtilgan A R．State-space prediction model for chaotic time series．Phys Rev E199858（2）：2640 ［9］ Jaditz TRiddick A．Time-series near-neighbor regression．Stud Nonlinear Dyn Econometr20004（1）：35 ［10］ Grassberger PProcaccia I．Characterization of Strange AttractorsPhys Rev Lett198350（5）：346 ·216· 北 京 科 技 大 学 学 报 第30卷