摘要
光电经纬仪目标跟踪系统主要用于外弹道跟踪测量,完成飞机和航空炸弹的飞行轨迹和弹道参数测量。由于被测目标小、暗、对比度差等特点,在远距离上进行弱小目标的检测,可以提高系统对目标反应的灵敏度,提供充足的反应时间。弱小目标检测与跟踪技术的研究无论在理论上还是在应用方面都具有重要的价值。因而远距离、低信噪比、强杂波下的弱小目标检测和跟踪问题成为当前一个既热门又困难的课题。本文针对目前靶场设备电视跟踪系统实现目标自动跟踪这一实际需要,对军事目标识别与跟踪算法问题进行了深入系统的研究。
在分析与目标检测相关的小波变换理论的基础上,为提高目标边缘检测效率,对目标图像进行增强和去噪的预处理方法进行了研究,首先研究了天空背景下(点、弱小)运动目标的识别问题。在各种背景的弱目标检测算法研究中,采用了最大类间方差(Otsu)分割的检测算法,对于不同大小的目标提出了两种不同的处理方法。在背景简单的小目标的预处理利用了中值滤波和Otsu相结合的方法;对于背景相对复杂的大目标的检测采用自适应门限,拉普拉斯(Log)滤波和Otsu分割的检测算法提取目标。自适应门限用于增强图像,使图像的背景灰度变得均匀;Log高通滤波器可以有效地去除背景;Otsu是经典的非参数,无监督自适应阈值选取方法,对图像经过阈值分割后,图像将变成包含少量可能目标点的二值图像。通过仿真实验表明,该算法能够有效地去除背景天空强浮云背景,具有计算量小等优点,能够很好地检测出目标。根据小波变换及多尺度分析的理论,分析了图像局部灰度特性,在小波变换方法的各种分析中,提出了双正交小波的弱目标提取方法,它的正交性和对称性,达到了最佳的滤波和检测效果,从中发现了点目标。
传统的边缘检测方法很难在精度与抗噪上达到满意的效果,对噪声相对敏感。本文在弱目标的边缘检测算法中提出两种方法,通过对形态学的研究,经中值和LOG滤波等图像预处理之后,膨胀腐蚀后的边缘检测算法能够有效地检测出图像的边缘,速度快,实现简单、能够较好地检测出边缘的特点;采用小波变换进行小波边缘检测的研究,利用七种不同的小波滤波器和不同的阈值进行分割来进行边缘检测,实验证明采用双正交小波的效果最好,实现了图像边缘的单象素。
粒子滤波通过非参数化的蒙特卡罗模拟方法来实现递推贝叶斯滤波,适用于任何能用状态空间模型表示的非线性系统。本文对粒子滤波理论及其实现方法进行了研究,通过模拟实验验证了其优于卡尔曼跟踪的性能。提出了一种基于双正交小波的边缘提取结合粒子滤波的跟踪方法,构建其跟踪框架。通过粒子数和系统状态方程的选择,实现了云层背景下用粒子滤波算法对背景简单的点目标和存在遮挡情况下的目标进行跟踪的过程,最后通过实验分析了影响跟踪精度的因素。实验证明,结合鲁棒性的小波检测方法和具有“多峰”描述的粒子滤波算法构造成的跟踪器,在运动目标存在局部遮挡等情况下能够实现稳定的目标跟踪。
结合多尺度Gabor滤波器和BP神经网络的基本理论,本文设计并实现了参数优化的Gabor滤波和BP神经网络的检测算法。根据Gabor滤波器具有的良好方向特性,首先确定方向参数,然后在每个特定方向进行最佳的单Gabor滤波器的参数搜索,采用粒子群优化PSO(Particle Swarm Optimization)方法得到的Gabor滤波器组,在性能上是接近最优的。提取48个纹理特征,采用主成分分析PCA(Principle ComponentAnalysis)降维处理,解决了Gabor滤波器应用中的瓶颈问题,即Gabor特征矢量维数较高,以及由此产生的较大计算量。该算法应用于目标识别时,不仅提高了识别的精度,而且克服了BP算法易陷入局部极小的缺陷。经过测试,本文提出的算法在检测图像时具有良好的准确性和鲁棒性,检测率达96.3%。
对于真彩多光谱图像和它的全色图像融合时出现的颜色扭曲现象,本文提出一种结合了IHS和小波技术的新的融合方法。采用多种小波进行实验,对取最大值的获取系数方法用取权值的方法进行替换。通过统计融合带和与其对应的原多光谱带的相关系数,来评价融合效果。实验表明在算法中用db4小波和取权值的获取系数方法,得到的融合效果最好。
Photoelectricity theodolite target tracking system is mostly used in tracking and measure of the outer trajectory, which can complete flight tracking or trajectory parameter of plane and aviation bomb. As measured object is small and dim, low contrast, the detection of puniness targets in long distance is a problem of crucial importance, because it can improve system sensibility to intruding targets and provide enough response time. Dim target recognition and tracking have important value whether in theory or in apply, which is a crucial step in infrared imaging guidance. Hence the small target recognition and tracking of long-range, low SNR (signal to noise ratio), strong clutter wave become a popular and difficult subject. In the paper, by the thorough and systemly study of military affairs target recognition and tracking method, which are solved with the realization in target automatic tracking of shooting range facility TV tracking system currently.
Basing on analysis and correlative theory of wavelet transform, firstly studying the method of target detection and edge detection based on wavelet transform in the paper. In order to improve efficiency of target edge detection, studying pretreatment method of image enhancement and denoising. In frist, the recognize problem of (point, dim) moving target are researched under the sky background in the paper. The detection of weak targets in the all kinds of background, adopting Otsu segmentation method, and two different methods are put forward. The small targets pretreatment use of the median filtering and Otsu segmentation in simple background; the large target pretreatment use of the adaptive threshold, Log filtering and Otsu segmentation, the target can be extracted. Adaptive threshold used to enhance the image so that the gray background of the image becomes uniformly; the Log filter for the high-pass can remove the background effectively; the variance between the largest category is a non-classical parameters, unsupervised adaptive threshold selection method, After threshold segmentation of the image, the image will become a binary image which may contain the target. The simulation experiments show that the algorithm can effectively remove the background and has the small amount of computing advantages, which can inspect targets very well. Base on theory of the wavelet transform and multi-scale analysis, analyzing image local gray grey level, in all kinds analysise of the wavelet transform, biorthogonal wavelet algorithm is proposed to deal with dim object, as good orthogonality and symmetry, realizing optimal filter and inspection are obtained, through this way, dim object can be found.
The conventional method of edge detection is difficult to attain satisfying effect between precision and anti-noise, and it is relatively sensitivety to noise. Two methods are brought forward in the edge detection algorithm of the weak target in the paper, through the studies of the morphological, after median and Log filter, a series of image preprocessing, using the edge detection algorithm of the expansion and corrosion can effectively detect the edge of the target. This method has the simple, better detection of the edge characters. Using the wavelet edge detection research through the wavelet transform, seven different wavelet filter and different threshold are used for edge detection. The experiment proved that adoptting of biorthogonal wavelet can gain the better result, and odd pels of image can be realized.
Particle filter realize recursive Bayesian filter via Monte Carlo simulation. The method is suitable for any non-linear system that could be represented with state model. The theory and method of Particle filter are studied in the paper, through simulate experiment validate its capability excel to kalman tracking. A method of biorthogonal wavelet edge extraction combining Particle filter is presented, and making up its frame of tracking. Through selecting state equation of system and particle number, which realize dot target in simple background and target in shelter complicated tracking used algorithm. At last through exprement the factor of affecting the tracking accuracy are analyzed. Experiment validate, the tracker with combining robust wavelet inspect method and having many apex descriptive particle filter algorithm, which can realize steady target tracking in moving target exist local shelter.
Combing the basic theory of multi-scale Gabor and BP neural net, in the paper parameter Optimizing Gabor filter and BP neural net recognize algorithm is designed. Based on Gabor better directional trait, at frist orient parameter, then searching optimal parameter of single Gabor filter in each aspecial direction, PSO optimal algorithm get Gabor filter team, which is close to optimal in the capability. In the son image distill 48 texture feature are extracted, adopting PCA (Principle Component Analysis) dimension reduction dispose, which can solve bottle-neck problem in the apply of Gabor filter, namely Gabor feature vector dimension is relatively high, as well as large computational complexity. when the algorithm is apllied to target, not only improve recognition precision, but also overcame the bug of BP algorithm get in minimum. By testing, the algorithm has better veracity and robust, and inspecting rate attain to 96.3%.
For the color distortion problem of the natural color multispectral images are fused with its panchromatic images, a new fusion approach is putted forward, which integrates the advantages of both the IHS and the wavelet techniques. In the experiment, multi-wavelet is adopted, when select the high-resolution coefficients, selectting the image that have the largest variance sum of the same level high-resolution coefficients, and select its high-resolution coefficients to be the high-resolution coefficients of the fusion image. Calculate the correlation coefficients between the fused bands and their corresponding original multispectral bands, to contrast the fusion results. It can be found that use db4 and Weight Coefficients approach in the algorithm, the fusion result is the best.
引文
[1]GJ-360光电经纬仪方案设计报告.长春光机所内部报告.2001
[2]宋建中,王延杰.跟踪测量电视系统[J]光学 精密工程,1995,vol 5(3):44-52
[3]王延杰 电视跟踪测量系统小型化的研究[D].中科院硕士论文,2000
[4]美、苏、英、法、西德等国军事电子装备研制概况及在武器系统中的应用 国防科工委情报局,电子工业部科技司,1987年12月,P.243-252
[5]吴能伟.光电经纬仪目标异面测量的误差分析[J].中国仪器仪表2006,(6):49-50.
[6]陈娟.现代靶场光电测量工程的发展现状[J].光机电信息,2002,1,22-27
[7]付晓炜.电视跟踪系统的研制及相关跟踪算法的研究[D].东南大学自动控制系,2004.
[8]陈振学,汪国有.基于自适应背景预测的红外小目标检测算法[J].激光与红外,2005,vol 35(8):608-612.
[9]吴一全,罗子娟.基于最小一乘背景预测的红外小目标检测算法[J].光电工程,2002,vol 35(4):12-16
[10]罗军辉,姬红兵,刘靳.一种基于空间滤波的红外小目标检测算法及其应用[J]红外与毫米波学报,vol 26(3),2007,209-212
[11]边肇祺,张学工等《模式识别》清华大学出版社,2000,1
[12]Marr D.Vision.Freeman WH.and Company.San Francisco,1982
[13]Barrow,H.G.Tenenbaum,J.M.Computational vision[J],Proc.Of IEEE,1981 Vol 69(5):572-595
[14]Aloimonos,J.Purposive and qualitative active vision.Proc.10th International Conference Of Pattern Recognition,1990.vol 1:346-360
[15]A1oimonos J.Weiss I.Bmdopadbay A.Active vision,International Journal of Computer Vision[J].1988,vol 1(4):333-356
[16]Annen F.Aggarwai J K.Model-based object recognition in dense range images a review.ACM Computing Surveys.1993,25(1):5-43
[17]Pollard SB.Pridmor TP.Geometrical modeling from multiple stereo.Int.J.of Robotics Researcb.1989,vol 8(4):3-32
[18]Lee K W Ryu S W.Lee S J.Park K T.Motion based object tracking with mobile Camera.Electronics Letters.1998,Vol 34(3):256-258
[19]Duric Z.Rivlin E.Rosenfeld A.Understanding object motion.Toward learning robots.1993,65-91
[20]Komarek T.Pirscb P.Array architecture from block matching algorithms.IEEE Trans.Circuits and System.1989,Vol 36:269-277.
[21]Hang H M.Cbou Y M.Cbeng S C.Motion estimation for video coding standards.Journal of VLSI Signal Processing Systems for Signal.Image and Video Technology.1997,Vol 17(2/3):113-136
[22]Leymarie F.Levine M D.tracking defonnable objects in theplane using an active contour model[J].IEEE Trans.Pattern Analysis and Machine Intelligence.1993,Vol 15(6):617-634
[23]Techmer A.Contour-based motion estimation and object tracking for real-time applications,proceedings of International conference on Image processing.2001,Vol 3:648-651
[24]Wang Y.Lee O.Active mesh-a feature seeking and tracking image sequence representation scheme[J].IEEE Trans.Image Processing.1994,Vol 3(5):610-624
[25]Meulemans P.Wilson R.Correcting velocity measurements by tracking of linear features[J].IEE Colloquium on Motion AnabIsis and Tracking.1999,101-106
[26]Kumar A,Bar-Shalom Y,and Oron E.,Precision Tracking based on Segmentation with Optimal Layering for Imaging Sensors[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,Vol 17(2):182-188.
[27]Salmond D.J.and Bircht H,A Particle Filter for Track-Before-Detect,In Proceedings of the American Control Conference,2001,Vol 5:3755-3760
[28]John L.A.and Krishnamurthy V,Performance Analysis of a Dynamic Programming Tracking Before Detection Algorithm,IEEE Trans,2003,Vol 38(1):228-242
[29]张惠娟,梁彦,程永梅等 运动弱小目标先跟踪后检测技术的研究进展[J].红外技术,2006,28(7):423-430
[30]Yang C,Duraiswam i R,Davis L.Efficient spatial-feature track ing via the mean-shift and a new similarity measure[C].IEEE Conference on Computer Vision and Pattern Recognition,San D iego,CA,2005:351-357
[31]Paragios N and Deriche R.Geodesic active contours and level sets for the detection and tracking of moving objects.IEEE Trans Pattern Analysis and Machine Intelligence,2000,22(3):266-280.
[32]Stauffer C,Grim sonW E L.Learning patterns of activity using real-time tracking[J].IEEE Trans on PAMI,2000,(22)8:747-757.
[33]Brais Martinez,Xavier Binefa.Piecewise affine kernel tracking for non-planar targets[J]Pattern Recognition 12(41 2008 3682-3691
[34]C.Hue,J.-P.L.Cadre,P.Pc'rez,Tracking multiple objects with particle filtering,IEEE Transactions on Aerospace and Electronic Systems 38(32)2002,791-812.
[35]S.Zhou,R.Chellappa,B.Moghaddam,Visual tracking and recognition using appearance-adaptive models in particle filters,IEEE Transactions on Image Processing.13(11)2004,1434-1456.
[36]Liu F,Ruan X E.Wavelet-based Diffusion Approaches for Signal Denoising[J].Signal Processing,2007,87:1138-1146.
[37]熊良才,史铁林,杨叔子.基于滤波器组的时频分布及其在故障诊断中的应用[J].中国机械工程,2003,14(19):1640-1642.
[38]Abdulhamit Subasi.Classification of EMG signals using wavelet neural network.Journal of Neuroscience Methods,2006,21(1),8-16.
[39]Morlet J.Wave propation and sampling theory and complex waves.Geophysics.1982,vol 47(2):222-236
[40]Grossmann A.and Morlet J.Decomposition of Hardy functions into square integrable wavelets of constant shape.SIAM J.Math.Anal,1984,Vol 15:723-736
[41]Y Meyer.Wavelets of algorithms and applications.NewYork:SIAM,1993
[42]S.Mallat.A theory for multiresolution signal decomposition:the wavelet representation.IEEE Trans PAM,1989,11(7):674-693
[43]Daubechies I.Orthononmal Bases of Compactly Supported Wavelets.Comm.Pure.Appl.Math.1998,Vol 41:909-996
[44]彭玉华《小波变换与工程应用》科学出版社2002,2
[45]Daubechies,W Sweldens.Factoring wavelet transforms into lifting steps.J.FourierAnal.1998,Vol 4(3):245-267
[46]Daubechies.The Wavelet Transform,Time-frequency Localization and Signal Analysis[J].IEEE Trans.On Information Theory.1990,Vol 36(5):961-1005
[47]Chui C.K.An Introduction to Wavelets.Academic Press,London,1992
[48]Sweldens W.The lifting scheme:A new philosophy in biorthogonal wavelet constructions[J].Proc.SPIE.1995,Vol.2569:68-79
[49]Daubechies,I.And Sweldens,W.Factoring wavelet transforms into lifting steps,J.Fourier Anal.Appl.1998,Vol.4(3):247-269.
[50]Yangqing Jia,Changshui Zhang.Front-view vehicle detection by Markov chain Monte Carlo method[J]Pattern Recognition[J],2008,Vol 42(3):313-321
[51]Wachter S and Nagel H-H.Tracking persons in monocular image sequences.Computer Vision and Image Understanding[J],1999,74(3):174-192.
[52]Paragios N and Deriche R.Geodesic active contours and level sets for the detection and tracking of moving objects.IEEE Trans Pattern Analysis and Machine Intelligence[J],2000,Vol 22(3):266-280.
[53]Lawton W,Lee S.L,Shen Z.WAn algorithm for matrix extension and wavelet construction.Mathematics of Computation[J].1996,Vol 65:723-737
[54]Coifman R.R.Wickhauster,Wavelets and adapted waveform analysis a toolkit for signal processing and numerical analysis.Proceedings of symposia in Applied Mathematics.1983,Vol 47:119-153
[55]Strela V Multiwavelets theory and application[D].MIT,1996
[56]Wornell,G.W.Oppenheim,A.V.Wavelet-based representations for a class of self-similar signals with application to fractal modulation.1992,Vol 38(2):785-800
[57]Guangyi Chen Applications of wavelet transforms in pattern recognition and de-noising,Dissertation for the academic degree of master,1999
[58]Stephane G.Mallat.Foveal approximations for singularities.Applied and Computational Harmonic Analysis.2003,Vol.14:133-180
[59]Stephane G.Mallat,J.Kalifa,B.Rouge.Deconvolution by Thresholding in Mirror Wavelet Bases[J].IEEE Trans on Image Processing,2003,Vol.12(4):446-457
[60]W Sweldens.The lifting scheme:a custom design construction of biorthogonal wavelets.Department of Mathematics,University of South Carolina:Industrial Mathematics Initiative.1994.7
[63]孙延奎《小波分析及其应用》机械工业出版社,2005.3
[64]S.Mallat.Zero-Crossings of a Wavelet Transform[J].IEEE Trans.On Information Theory.1991,Vol 7(4):1019-1033
[65]Otsu N.A threshold selection method f rom gray-level histogram[J].IEEE Trans on Systems,Man and Cybernetic,1979,vol 9(1):62-66.
[67]Adam Baumberg,David Hogg.Generating spatial temporal models from examples.Image and Vision Computing.1996,525-532.
[68]Canny J.A Computational Approach to Edge Detection.IEEE on PAMI.1986,vol 8(6),669-698.
[69]J.M.S.Prewitt.Object enhancement and extraction in B S.Lipkin and A.Rosenfeld eds.Picture Processing and Psychopictorics,1970
[70]R.M.Haralick.Digital step edges from zero crossing of second directional derivatives[J].IEEE Trans on PAMI.1984,vol 6(1):58-68
[71]R.M.Haralick Ridges and valleys on digital images[J].CVGIP.1983,22:28-38
[72]Yong Wu,Yuanjun He,Hongming Cai Optimal threshold selection algorithm in edge detection based on wavelet transform Image and Vision Computing.2005,vol 23:1159-1169
[73]Jun Li,A wavelet approach to edge detection,MASTER thesis,Sam Houston State University_Hunts ville_Texas_August 2003
[74]A.Rosenfeld.Computer Vision:A Source of Models for Biological Visual Process[J].IEEE Trans on Biomed Eng.1989,vol 36(1):83-94
[75]A.P.Witkin.Scale space filtering[C].Proc.lnt.Joint Conf.Artificial Intelligent 1983,1019-1022.
[76]C.K.Chui,J.A.Lian.A construction of compactly supported symmetric and orthonormal wavelets with scale 3[J].Applied and Computational Harmonic Analysis.1995,(2):21-52
[77]Yuping Wang,Yuanlong Cai.Construction and properties of B-spline wavelet filters for multiscale edge detection[C]Image Processing.1995,vol 2:145-148
[78]Xu chao.Analysis of morphological quasi-circular structure elements and skeleton[J].ACTA Electronic Sinica.1999,vol 27(8):78-81
[79]Maragos.Tutorial on advances in morphological image processing and analysis[J].Optical Engineering.1987,vol 26(7):623-632
[80]S.Mallat and S.Zhong.Characterization of signals from multiscale edges[J].IEEE Trans.Patt.Anal.and Mach.intell,July 1992,voi 14(7):710-732
[81]魏海、沈兰荪.反对称双正交小波应用于多尺度边缘提取的研究[J],电子学报,2002,30(3):313-316
[82]Deriche R.Using Canny's Criteria to Derive a Recursively Implemented Optimal Edge Detection[J].International Journal of Computer Vision.1987,vol 1(2):167-187
[83]Donoho D.L.De-noising by soft-thresholding[J].IEEE.Trans Inform Theory.1995,vol 41(3),613-627.
[84]Chang,S.G Bin Yu Vetterli,M Adaptive wavelet thresholding for image denoising and compression[J]IEEE Trans.Image Processing.2000,Vol 9(9):1532-1546
[85]Ming-Yu Shih,Din-Chang Tseng,A wavelet-based multiresolution edge detection and tracking Mathematical and Computer Modelling.2005,Vol 23(4):441-451
[86]王敬东,徐亦斌,李鹏.图像小波边缘检测中边界处理的研究[J],计算机工程,2007,33(5):161-163
[87]Unser M,Aldroubi A.Eden M.On the asymptotic convergence of B-spline wavelets to Gabor function[J].IEEE Trans.1992,vol 38(2):864-872
[88]彭楠,何小海,汪华章.基于样条小波和Gabor小波的边缘检测[J],成都信息工程学院学报,2007,22(2):195-197
[89]G.Chang,B.Yu,and M.Vetterli,Spatially adaptive wavelet thresholding with context modeling for image denoising,IEEE Trans.Image Processing.2000,vol,9(9):1522-1531
[90]黄凤贤,王保保.基于自动确定阈值的小波边缘检测技术研究计算机仿真[J],2004,21(8):155-157
[91]刘宏兵等.图像小波边缘提取中阈值选取的一种自适应方法[J],西安电子科技大学学报,2000,27(3):294-296
[92]Daubechies,I.And Sweldens,W,Factoring wavelet transforms into lifting steps,J.Fourier Anal.Appl.1998,Vol.4:247-269.
[93]A.Secker and D.Taubman,Highly scalable video compression using a lifting-based 3D wavelet transform with deformable mesh motion compensation,Proc.ICIP.2002,vol.3:749-752,
[94]H.J.A.M.Heijmans and G.Piella and B.Pesquet-Popescu,Building adaptive 2D wavelet decompositions by update lifting[J],IEEE International Conference,Image Processing.2002,Vol 1:397-400
[95]Jorge Badenas,Bober M,Pla F.Motion-based segmentation and region tracking in image sequences.2001,vol 34(3):661-670
[96]Jorge Badenas,Jose Miguel,Sanchiz,Filiberto Pla.Motion-based segmentation and region tracking in image sequences[J].Pattern Recognition.2001,vol 34(3):661-670
[97]Comaniciu D.,Ramesh V,and Meer,P,Kernel-based Object Tracking[J],IEEE Transactions on Pattern Analysis and Machine Intelligence.2003,vol 25(5):564-577
[98]Liu T.L.and Chen H.T.,Real-time Tracking Using Trust-region Methods[J],IEEE Transactions on Pattern Analysis and Machine Intelligence.2004,vol 26(3):397-402
[99]Nummiaro K,Koller-Meier E,Svoboda T.,Roth D,Color-based Object Tracking in Multi-Camera Environments[C],In Proceedings of the 25th Pattern Recognition Symposium DAGM 2003,591-599.
[100]Li P.H,Chaumette F,and Tahri O,A Shape Tracking Algorithm for Visual Servoing[J],In Proceedings of IEEE International Conference on Robotics and Automation,April.2005,2858-2863.
[101]Paragios N and Deriche R.Geodesic active contours and level sets for the detection and tracking of moving objects[J].IEEE Trans Pattern Analysis and Machine Intelligence.2000,vol 22(3):266-280.
[102]Black M J,Anadan P.The robust estimation of multiple motions;Parametric and piece wise smooth flow fields[J].Computer Vison and Image Understanding.1996,vol 63(1):75-104.
[103]Kim W.,Lee C.Y,and Lee J.J,Tracking Moving Object Using Snake's Jump based on Image Flow[J],MECHATRONICS.2001,vol 11(2):199-226.
[104]Jung S K,Wohn K Y.A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views[J].Pattern Recognition Letters.1998,(19):499-512
[105]Hang Z,Faugeras O D.Three-dimensional motion computation and object segmentation in a long sequence of stereo frames[J].International Journal on Computer Vision(IJCV).1992,vol 7(3):211-241.
[106]Welch G.and Bishop G.,An Introduction to the Kalman Filter,Technical Report,University of North Carolina at Chapel Hill,August,1997.
[107]Christian P,George Casella.Introduction to Monte Carlo Statistical Methods[M].George Casella University of Florida.New-York:Springer-Verlag,2004
[108]G.Kitagawa.Monte Carlo filter and smoother for non-Gaussian nonlinear state space models[J].Journal of Computational and Graphical Statistics.1996,Vol.5(1):1-25
[109]A.Doucet,N.Gordon,and V.Krishnamurthy,Particle filters for state estimate on of jump Markov linear systems[J].IEEE Trans.Signal Processing.2001,vol.49(3):613-624
[110]强世锦,荣健,滑玉.基于粒子预测的非线性状态目标跟踪算法[J],武汉理工大学学报,2008,30(2).113-115
[111]侯一民,郭雷,伦向敏.运动背景下基于粒子滤波的目标跟踪[J],计算机工程与应用,2007 43(8),62-64,102
[112]姚剑敏,孙俊喜,孙中森等.基于粒子滤波的小波特征跟踪方法研究[J],计算机仿真,2006,23(1),94-95,304-305
[113]Jacek Czyz,Branko Ristic,Benoit Macq.A particle filter for joint detection and tracking of color objects[J],Image and Vision Computing,2007,25(8),1271-1281
[114]Sukwon Choi,Daijin Kim.Robust head tracking using 3D ellipsoidal head model in particle filter[J],Pattern Recognition,2008,41(9),2901 2915
[115]Gabor D.Theory of communication[J].Journal of the Institute of Electrical Engineers.1946,vol 93(26):429-457.
[116]Marcel ja S.Mathematical description of the responses of simple cortical cells[J].Journal of Optical Society ofAmerica.1980,vol 70(11):1297-1300.
[117]Daugman J G.Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile.Vision Research.1980,vol 20(10):847-856.
[118]Jones J P,Palmer L A.An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex[J].Journal ofNeurophysiology.1987,vol 58(6):1233-1258.
[119]Eberhart,R.and Kennedy,J.A new optimizer using particle swarm theory,Proc.6 Int.Symposium on Micro Machine and Human Science.1995,39-43
[120]Eberhart R C,Shi Y Particle swarm optimization:developments,applications and resources[C].Proc IEEE Int'1 Conf on Evolutionary Computation,Seoul,Korea.2001,vol 1(1):81-86.
[121]Shi Y,Eberhart R C.A modified particle swarm optimizer[C].Proc IEEE lnt' 1 Conf.On Evolutionary Computation.1998,vol 4(9):69-73
[122]Tai Sing Lee.Image representation Using 2D Gabor wavelet[J].IEEE Trans on Pattern Analysis and Machine Intelligence.1996,vol 18(10):959-971
[123]A.C.Bovik,Analysis of multichannel narrowband filters for image texture segmentation[J],IEEE Trans.SignalProcessing.1991,vol.39(9):2025-2034,
[124]R.Mehrotra,Gabor filter-based edge detection[J].Pattern Recognition.1992,vol 25(12):1479-1494
[125]A.C.Bovik,M.Clark,and W.S.Geisler,Multichannel texture analysis using localized spatial filters,IEEE Trans,PAMI,1990,vol.12(1):55-73
[125]汤国安,张友顺,刘咏梅,谢元礼,杨昕,刘爱利.遥感数字图像处理[M].北京:科学出版社,2004.2
[127]承继成,郭华东,史文中.遥感数据的不确定性问题[M].北京:科学出版社,2004.140-141.
[128]黎新亮,赵书河,柯长青,遥感图像融合定量评价方法及实验研究[J],遥感技术与应用,2007,22(3):460-465
[129]张科 李言俊,像素级多分辨图像融合技术概述[J],系统工程与电子技术,2004,26(1):137-141
[130]王仁礼 王惠,用于图像融合的IHS变换方法的比较[J],测绘学院学报,2000,12,17(4):269-272
[131]Yun Zhang Gang Hong,An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images[J],Information fusion.2005,vol 6(3):225-234
[132]Gang Hong,Yun Zhang,High resolution image fusion based on Wavelet and IHS transformations[J],Department of Geomatics Engineering.2003,vol 22(23):99-104
[133]边境 周庆利,一种多光谱图像和全色图像融合算法,微电子学与计算机,2005,22(6):5-11
[134]Tao Guanqun Li Dapeng Lu Guanghua,On Image Fusion Based on Different Fusion Rules of Wavelet Transform[J],光子学报,2004,33(2):221-224