基于声纳图像多分辨率处理的目标检测与跟踪
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摘要
伴随着海洋矿产资源开发、海洋工程、海洋开发等领域的日新月异的发展,工作在水下机器人、遥控潜水器等多种载体上的声纳系统除了需要胜任极端环境下的工作外,还需要高分辨率的成像、目标识别能力以及多个声纳图像的匹配及识别能力,以满足区域的高分辨率地形地貌测量、准确分辨海底起伏剧烈区域以及沉底小目标和水中目标的探测等需求。由于声纳设备成像的非线性和水下声场环境的复杂性,所采集到的水下声纳图像具有背景噪声包含的灰度级比较丰富,声纳图像的目标区域灰度级相对较少等特点,这些特点对后续的声纳图像目标检测与定位跟踪等工作带来了很大的难度。多分辨率分析属于多尺度细化分析,通过基函数的平移、伸缩等运算对信号进行局域时频分析,从信号中能有效地提取信息,是信号处理的研究热点和前沿课题,是目前国际公认的信号与信息处理领域的高新技术。它在信号滤波、图像去噪、图像分割、图像识别等领域的应用越来越多地受到人们的重视。
本论文主要研究多分辨率分析及其在2D声图像及3D序列处理中的应用,主要在声纳图像的去噪增强等预处理、图像区域分割、运动目标分割、目标及敏感区域特征提取、目标跟踪定位和多声纳场景目标匹配等方面进行了研究。
首先,对常用的多分辨率变换方法及传统的图像去噪方法进行了概括性的介绍与分析。阐述了声纳的成像机理及水下目标声图像的统计特性。针对水下目标声纳图像不易判别边缘及细节、对比度差等特点,提出了三种基于多分辨率工具的声图像去噪方法,其中包括基于抽样矩阵的Surfacelet变换水下目标声图像去噪方法、水下目标声图像分块自适应降噪方法、基于三维上下文模型的水下目标声图像降噪方法。这些方法具有平移不变性,多向性,图像及序列的空域与时域相关信息利用率高,并且通过仿真实验,验证了这些去噪方法的优越性和有效性。
其次,对经典的图像分割方法进行了概括性的介绍与分析。针对高分辨声纳图像富含大量随机噪声、目标区域的边缘不清且断续等特点,基于多分辨率工具以边缘检测算法进行声图像区域识别及修复,提出了基于双边滤波和Surfacelet的边缘检测的声图像分割方法。分割实验表明本文算法能有效去除混响等噪声区域的同时,显著地提高了图像的视觉效果,尤其是在边缘、细节保持方面有一定程度的提高。与马尔科夫随机场(MRF)模型分割方法、小波聚类分割方法比较,获得了更为准确的分割结果。
再次,对传统的基于图像及图像序列的运动目标检测及跟踪技术方法进行了概括性的介绍与分析。针对高分辨声纳图像特点,提出了静态背景下基于LIP模型及光流法的运动目标检测算法、复杂背景下基于Surfacelet的声图像序列运动目标检测算法、基于可变图像模版匹配及Surfacelet变换的声图像序列目标跟踪算法。实验表明本文算法在声图像存在较多混响等噪声区域的条件下,能有效检测序列中运动目标。基于可变图像模版匹配及Surfacelet变换的声图像序列目标跟踪算法,与mean shift跟踪算法、SIFT特征匹配的跟踪算法比较,获得了更为准确的跟踪结果。在匹配稳定的情况下,能够及时利用尺度不变特征估计目标尺度变化,克服传统的基于核的跟踪方法不能有效估计目标的尺度及旋转量的缺陷。
最后,对常用的多摄像机图像目标匹配方法进行了概括性的介绍与分析。针对高分辨声纳图像序列富含大量信道噪声、散射噪声,不易判别边缘及细节、对比度差等特点,依据人眼视觉系统在图像的不同空间频率及不同区域敏感度特性,提出了基于区域SIFT描述子及Surfacelet的声纳目标匹配算法。实验表明,本文算法能有效提取声图像目标SIFT描述子特征,能够实现多声纳场景目标匹配。与全局应用SIFT描述子进行声图像目标匹配方法相比,基于区域SIFT描述子及Surfacelet的多声纳场景目标匹配算法SIFT描述子数据总量(SIFT特征点数及SIFT描述子维数)较低,计算复杂度有较大降低,并且具有较高的匹配精度。
综上所述,本文研究了基于多分辨率方法在2D声图像及3D序列处理中的应用,并针对目前该领域中存在的不足及需求,设计相应算法进行改进与功能实现。仿真实验证实,本文所提出的改进方案和应用的算法,均能够获得很好的效果。
With development of ocean resource exploitation and ocean engineer, the sonar need multi-resolution image and target recognition ability and multi-sonar matching and recognize ability besides of working at extremeness environment in order to requirement of high resolution terrain relief measurement and small target detection. Due to sonar equipment imaging no linearity and complexity of under water sound field environment, the background noise of sonar image include enough gray levels and the target area gray levels is comparatively less. The characters bring difficulty for following sonar image target detection and orientation track. Multi-resolution analysis tool can multi-scale details analysis by basis function expansion, translation and other computing, and it can effectively extract information from signals. At present, Multi-resolution analysis is international acknowledged advanced technology in the domain of information and signal processing. Meanwhile it is the front a question for discussion and study hotspot. More and more people attach importance to the application of multi-resolution analysis in the domains such as signal filtering, image denoising, image segmentation and image recognition etc.
Multi-resolution analysis and its application in 2D sonar image and 3D sonar sequence processing are investigated in detail in this dissertation. The main work can be summarized on sonar image denoising, image area and movement target division, target and sensitivity area character distill, target tracking and orientation and multi-sonar scenes target matching etc.
Firstly, the concepts and denoising methods in common use multi-resolution transform are recapitulative introduced and analyzed. At the same time, the imaging mechanism and the statistics characteristic of underwater acoustic image is expatiated. Through studying acoustic image of character which is bad contrast and deteriorates edges and detail, three the acoustic image denosing methods based on multi-resolution analysis are proposed. They are Sonar image denoising method based on sample matrix and Surfacelet transform, block adaptive denoising method of acoustical imaging and the sonar image denoising method based on 3D context model respectively. These methods are characterized by multi-direction option, parallel information processing, high efficiency of information using at air space and time space, and fusing the enhanced on multi-resolution. By the simulation results, the effectiveness and superiority of the methods are proved.
Secondly, the classical image segmentation methods are recapitulative introduced and analyzed. Aim to character of mass random noise and target area edge blur of multi-resolution sonar image, which is recognized and restored based on multi-resolution and edge detection. The sonar image segmentation arithmetic is proposed based on spatial filter and Surfacelet transform edge detection. By the simulation results, the effectiveness and superiority of the methods are proved. It can obtain good segmentation result than MRF model segmentation and Wavelet clustering segmentation and protect edge and detail and improve visual effect.
Thirdly, the classical movement target tracking methods based on image and image sequence are recapitulative introduced and analyzed. Aim to character of sonar image, the movement target tracking arithmetic based on LIP model and light flow, the sonar image sequence movement target tracking arithmetic based on Surfacelet transform at complex background, the image sequence movement target tracking arithmetic based on changeable stencil and Surfacelet transform are proposed. The more exact result is obtained the image sequence movement target tracking arithmetic based on changeable stencil and Surfacelet transform than mean shift track arithmetic. Under conditions of matching steady, it can take full use of scale fixedness character to estimate scale change, but mean shift track arithmetic can not efficiently estimate target scale and rotate value.
Finally, the classical multi-cameras image matching methods are recapitulative introduced and analyzed. Aim to sonar image character, the sonar target matching arithmetic based on area SIFT description and Surfacelet transform according to sensitivity character to image different space frequency and vary areas of human eye system. The experimental results show that the arithmetic can efficiency distill sonar image target SIFT description character and realize target matching at multi-sonar scene. Its compute complexity is lower and matching precision is higher than whole application SIFT description.
In conclusion, on the base of multi resolution analysis, this paper had researched on multi resolution analysis and its application in 2D sonar image and 3D sonar sequence processing. Aiming at the shortcoming of the region, the improved algorithms were proposed. Experimental results indicated that the adopted intelligent optimization algorithms and the proposed methods could attain good results.
本论文主要研究多分辨率分析及其在2D声图像及3D序列处理中的应用,主要在声纳图像的去噪增强等预处理、图像区域分割、运动目标分割、目标及敏感区域特征提取、目标跟踪定位和多声纳场景目标匹配等方面进行了研究。
首先,对常用的多分辨率变换方法及传统的图像去噪方法进行了概括性的介绍与分析。阐述了声纳的成像机理及水下目标声图像的统计特性。针对水下目标声纳图像不易判别边缘及细节、对比度差等特点,提出了三种基于多分辨率工具的声图像去噪方法,其中包括基于抽样矩阵的Surfacelet变换水下目标声图像去噪方法、水下目标声图像分块自适应降噪方法、基于三维上下文模型的水下目标声图像降噪方法。这些方法具有平移不变性,多向性,图像及序列的空域与时域相关信息利用率高,并且通过仿真实验,验证了这些去噪方法的优越性和有效性。
其次,对经典的图像分割方法进行了概括性的介绍与分析。针对高分辨声纳图像富含大量随机噪声、目标区域的边缘不清且断续等特点,基于多分辨率工具以边缘检测算法进行声图像区域识别及修复,提出了基于双边滤波和Surfacelet的边缘检测的声图像分割方法。分割实验表明本文算法能有效去除混响等噪声区域的同时,显著地提高了图像的视觉效果,尤其是在边缘、细节保持方面有一定程度的提高。与马尔科夫随机场(MRF)模型分割方法、小波聚类分割方法比较,获得了更为准确的分割结果。
再次,对传统的基于图像及图像序列的运动目标检测及跟踪技术方法进行了概括性的介绍与分析。针对高分辨声纳图像特点,提出了静态背景下基于LIP模型及光流法的运动目标检测算法、复杂背景下基于Surfacelet的声图像序列运动目标检测算法、基于可变图像模版匹配及Surfacelet变换的声图像序列目标跟踪算法。实验表明本文算法在声图像存在较多混响等噪声区域的条件下,能有效检测序列中运动目标。基于可变图像模版匹配及Surfacelet变换的声图像序列目标跟踪算法,与mean shift跟踪算法、SIFT特征匹配的跟踪算法比较,获得了更为准确的跟踪结果。在匹配稳定的情况下,能够及时利用尺度不变特征估计目标尺度变化,克服传统的基于核的跟踪方法不能有效估计目标的尺度及旋转量的缺陷。
最后,对常用的多摄像机图像目标匹配方法进行了概括性的介绍与分析。针对高分辨声纳图像序列富含大量信道噪声、散射噪声,不易判别边缘及细节、对比度差等特点,依据人眼视觉系统在图像的不同空间频率及不同区域敏感度特性,提出了基于区域SIFT描述子及Surfacelet的声纳目标匹配算法。实验表明,本文算法能有效提取声图像目标SIFT描述子特征,能够实现多声纳场景目标匹配。与全局应用SIFT描述子进行声图像目标匹配方法相比,基于区域SIFT描述子及Surfacelet的多声纳场景目标匹配算法SIFT描述子数据总量(SIFT特征点数及SIFT描述子维数)较低,计算复杂度有较大降低,并且具有较高的匹配精度。
综上所述,本文研究了基于多分辨率方法在2D声图像及3D序列处理中的应用,并针对目前该领域中存在的不足及需求,设计相应算法进行改进与功能实现。仿真实验证实,本文所提出的改进方案和应用的算法,均能够获得很好的效果。
With development of ocean resource exploitation and ocean engineer, the sonar need multi-resolution image and target recognition ability and multi-sonar matching and recognize ability besides of working at extremeness environment in order to requirement of high resolution terrain relief measurement and small target detection. Due to sonar equipment imaging no linearity and complexity of under water sound field environment, the background noise of sonar image include enough gray levels and the target area gray levels is comparatively less. The characters bring difficulty for following sonar image target detection and orientation track. Multi-resolution analysis tool can multi-scale details analysis by basis function expansion, translation and other computing, and it can effectively extract information from signals. At present, Multi-resolution analysis is international acknowledged advanced technology in the domain of information and signal processing. Meanwhile it is the front a question for discussion and study hotspot. More and more people attach importance to the application of multi-resolution analysis in the domains such as signal filtering, image denoising, image segmentation and image recognition etc.
Multi-resolution analysis and its application in 2D sonar image and 3D sonar sequence processing are investigated in detail in this dissertation. The main work can be summarized on sonar image denoising, image area and movement target division, target and sensitivity area character distill, target tracking and orientation and multi-sonar scenes target matching etc.
Firstly, the concepts and denoising methods in common use multi-resolution transform are recapitulative introduced and analyzed. At the same time, the imaging mechanism and the statistics characteristic of underwater acoustic image is expatiated. Through studying acoustic image of character which is bad contrast and deteriorates edges and detail, three the acoustic image denosing methods based on multi-resolution analysis are proposed. They are Sonar image denoising method based on sample matrix and Surfacelet transform, block adaptive denoising method of acoustical imaging and the sonar image denoising method based on 3D context model respectively. These methods are characterized by multi-direction option, parallel information processing, high efficiency of information using at air space and time space, and fusing the enhanced on multi-resolution. By the simulation results, the effectiveness and superiority of the methods are proved.
Secondly, the classical image segmentation methods are recapitulative introduced and analyzed. Aim to character of mass random noise and target area edge blur of multi-resolution sonar image, which is recognized and restored based on multi-resolution and edge detection. The sonar image segmentation arithmetic is proposed based on spatial filter and Surfacelet transform edge detection. By the simulation results, the effectiveness and superiority of the methods are proved. It can obtain good segmentation result than MRF model segmentation and Wavelet clustering segmentation and protect edge and detail and improve visual effect.
Thirdly, the classical movement target tracking methods based on image and image sequence are recapitulative introduced and analyzed. Aim to character of sonar image, the movement target tracking arithmetic based on LIP model and light flow, the sonar image sequence movement target tracking arithmetic based on Surfacelet transform at complex background, the image sequence movement target tracking arithmetic based on changeable stencil and Surfacelet transform are proposed. The more exact result is obtained the image sequence movement target tracking arithmetic based on changeable stencil and Surfacelet transform than mean shift track arithmetic. Under conditions of matching steady, it can take full use of scale fixedness character to estimate scale change, but mean shift track arithmetic can not efficiently estimate target scale and rotate value.
Finally, the classical multi-cameras image matching methods are recapitulative introduced and analyzed. Aim to sonar image character, the sonar target matching arithmetic based on area SIFT description and Surfacelet transform according to sensitivity character to image different space frequency and vary areas of human eye system. The experimental results show that the arithmetic can efficiency distill sonar image target SIFT description character and realize target matching at multi-sonar scene. Its compute complexity is lower and matching precision is higher than whole application SIFT description.
In conclusion, on the base of multi resolution analysis, this paper had researched on multi resolution analysis and its application in 2D sonar image and 3D sonar sequence processing. Aiming at the shortcoming of the region, the improved algorithms were proposed. Experimental results indicated that the adopted intelligent optimization algorithms and the proposed methods could attain good results.
引文
[1]焦李成,侯彪,刘芳对.基函数网络逼近：进展与展望.工程数学学报.2002,,19(1)：21-36页
[2]邓承志,汪胜前,钟华,刘祝华,邹道文.基于Contourlet变换的图像去噪算法.电视技术.2004,10：21-22,42页
[3]贾建,焦李成.数字脊波变换的实现与一种改进方法.计算机研究与发展.2006,43(1)：115-119页
[4]王文波,羿旭明,费浦生.基于曲波系数相关性的去噪算法.光电子激光.2006,17(12)：1519-1523页
[5]尚政国,赵春晖,刘金梅.多分辨率域HMT图像去噪增强算法.哈尔滨工程大学学报.2007,28(6)：981-986页
[6]侯一民,郭雷.一种基于马尔可夫随机场的SAR图像分割新方法.电子与信息学报,2007,29(5)：1069-1072页
[7]劳丽.聚类算法研究及在图像分割中的应用.华南理工大学博士论文,2006.
[8]戴剑彬,张大力.图像分析中的松弛标记法.中国图象图形学报,1998,3(2)：97-99.
[9]汪海洋,潘德炉,夏德深.二维Otsu自适应阈值选取算法的快速实现.自动化学报,2007,33(9)：968-971.
[10]Q Yu,A Clausi. IRGS:Image segmentation using edge penalties and region growing. IEEE transactions on pattern analysis and Machine Intelligence,2008.30(12): 2126-2139.
[11]Koss E J, Newman D F, Johnson T K, et al. Abdominal organ segmentation using texture transforms and a hopfield neural network. IEEE Transactions on medical imaging,1999,18(7):640-648.
[12]Sridhar B, Phatak A, Chatterji G. Scene segmentation of natural images using texture measures and back propagation [C]//Artificial Neural Net works. NASA, Tech. Memo. Brighton, UK,1993:200-204.
[13]王兆虎,刘芳,焦李成.一种基于视觉特性的遥感图像分割.计算机学报,2005,28(10)：1686-1691.
[14]Minh N Do, Martin Vetterli. The contourlet transform:an efficient directional multiresolution image representation. IEEE Trans. Image Proc.,2005,14(2):2091-2106.
[15]GaoHa,i SiuWan-Chin, Hou Chao-Huan. Improved techniques for automatic image segmentation. IEEE Transactions on Circuits and Systems forVideoTechnology,2001, 11(12):1273-1280.
[16]Salembier P, PardasM. Hierarchical morphological segmentation for image sequence coding. IEEE Transactions on Image Processing,1994,3(5):639-651.
[17]R. Callaghan and D. Bul,l"Combined morphological-spectral unsupervised image segmentation,"[J]. IEEE Trans. Image Process,2005,14(1):49-62.
[18]M.N.Do,"Directionalmultiresolution image representations,"Ph.D. dissertation, SchoolCompu.t Commun. Sc.i. SwissFed. Ins.t Techno.l, Lausanne, Switzerland,2001.
[19]M.Sammouda, R.Sammouda, N.Niki,K. Mukai, Segmentation and analysis of liver cancer pathological color image based on artificial neural networks, Proceedings of IEEE 1999 International Conference on Image Processing,1999,Kobe,Japan,Vol.3,pp:392-396.
[20]R.Sammouda, M.Sammouda, Improving the performance of Hopfield neural network to segment pathological liver color images, International Congress Series,Vol.1256, pp:232-239,2003.
[21]S.H.Ong,N.C.Yeo,K.H.Lee,Y.V.Venkatesh,D.M.Cao,Segmentation of color images using a two-stage self-organizing network,Image and Vision Computing,Vol.20,No.4, pp:279-289,2002.
[22]P.Lescure,V.M.Yedid,H.Dupoisot,G.Stamon,Color segmentation on biological mi-croscope images,Proceeding of SPIE,Application of Artificial Neural Networks in Image Processing Ⅳ,1999,San Jose, California, USA, Vol.3647,pp:182-193.
[23]吴晓岩,RPCL算法在自动化图像分割中的应用,北京大学硕士论文,2003.
[24]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,25(3)
[25]代科学,李国辉,徐丹,袁见.监控视频运动目标检测减背景技术的研究现状和展望.中国图像图形学报,2006,11(7)
[26]A Lipton,H Fujiyoshi,R Patil. Moving target classification and tracking from real-time videl. IEEE Workshop on Application of Computer Vision,Princeton,NJ,1998.129-136
[27]L Wang, W Hu, T Tan. Recent developments in human motion analysis. In proceedings of International Conference on Pattern Recogniting, France,2003,36(3):585-601
[28]R Collions, A system gor cideo surceillance and monitoring VASM final repert.CMU-RI-TR-00-12,Robotic Institute Carngie Mellon University,2000
[29]A Verris, S Uras, E De Micheli. Motion segmentation from Optical FlowIn proceedings of the 5 th Alvey Vision Conference,1989.209-214
[30]J Barron, D Fleet, S Beauchemin. Perfrmance of Optical Flow Techniques.International Journal of Computer Vision,1994,12(1):42-77
[31]J Heikkila,0 Silven.A real-time system for monitoring of cyclists and pedestrians,In proceedings of 2th IEEE Workshop on Visual Surveillance,Colorado,1999.74-81
[32]R Cutler, L Davis. View-Based Detection and Analysis os Periodic Motion. In proceedings of Intenational Conference on Pattern Recognition Australia,1998:495-500
[33]R Cucchiara, M Picacardi, A Prati. Detecting moving objects, ghosts and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1337-1342
[34]Lin Hongwen, Tu Dan,Li Guo-hui. Moving objects detection method based on statistical background model. Computer Enginering,2003,29(16):97-99
[35]Andrea Canallaro, Olivier Steiger, Touradj Ebrahimi. Multple Video Object Tracking in Complex Scenes. IEEE Multinedia 02,France,Juan-les-Pins,pp:523-532,2002
[36]Bramberger M,Quaritseh M, Winkler T,et al. Integrating Multi-Camera Tracking into a Dynamic Task Allocation System for Smart Cameras.IEEE Conference on Advanced Videl and Signal Based Surveillance,2005:474-479
[37]Cipolla R. Pentland A. Computer Vision for Human-Machine Iteration. Cambridge University Press,1998
[38]Blake A,Isard M. Active Contours.Springer-Verlag,1998
[39]吴健康,肖玉.计算机视觉基本理论和方法,合肥：中国科技大学出版社
[40]Hu W M, Tan T N,Wang L, et al. A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions on SysTEM Man and Cybernetics,2004,34(3):334-351
[41]KELL Y P H, KATKERE A, KURAMURA D Y, et al. An architecture for multiple perspective interactive video//Proc of the 3nd ACM International Conference on Multimedia. San Francisco:[s.n.],1995:2012212.201-202
[42]ZHOU Quming, A GGARWAL J K. Object t racking in an outdoor environment using fusion of features and cameras. Image and Vision Computing,2006,24 (11): 124421255.1244-1255
[43]XU M, ORWELL J, LOWEYL, et al. Architectureand algorithms for t racking football players with mul2 tiple cameras. Image and Signal Processing,2005,152 (2):2322241.232-241
[44]JAVERD O, RASHEED Z, SHAFIQUE K, et al. Tracking across multiple cameras with disjoint views//Proceedings of IEEE International Conference on Computer Vision. Nice:[s. n],2003:1652171.165-171
[45]HU Wei-Ming et al. Principal axis-based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28(4):663-671
[46]Chang T H, Gong S, Ong E J. Tracking multiple people under occlusion using multiple cameras//Proceedings of the British Machine Vision Conference. Bristol, UK,2000: 566-575
[47]Krumm J et al. Multi-camera multi-person tracking for easy living//Proceedings of the IEEE International Workshop Visual Surveillance. Dublin, Ireland,2000:3-10
[48]Mittal A, Davis L S. M2Tracker:A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo//Proceedings of the European Conference on Computer Vision. Copenhagen, Denmarky,2002:18-36
[49]Bollobas B, Thomason A G. Random Graphics of Small Order. North-Holland, Amsterdam,1985:47-97
[50]Avidan S, Moses Y, Moses Y. Centralized and distributed multi-view correspondence. International Journal on Computer Vision,2007,71(1):49-69
[51]Tsutsui H, Miura J, Shirai Y. Optical flow-based person tracking by multiple cameras//Proceedings of the IEEE Conference on Multisensor Fusion and Integration in Intelligent Systems. Baden-Baden, Germany,2001:91-96
[52]Orwell J, Remagnino P, Jones G A. Multiple camera color tracking//Proceedings of the IEEE International Workshop Visual Surveillance. Fort Collins, USA,1999:14-24
[53]Cai Q, Aggarwal J K. Tracking human motion in structured environments using a distributed-camera system. IEEE Transactions on Pattern Recognition and Machine Intelligence,1999,21(11):1241-1247
[54]Lowe David G. Object recognition from local scale-invariant features//Proceedings of the International Conference on Computer Vision. Corfu, Greece,1999:1150-1157
[55]Brown M, Lowe D. Recognizing panoramas//Proceedings of the International Conference on Computer Vision. Nice, France,2003:1218-1225
[56]Daubechies I. Orthonormal bases of compactly supported wavelets. Comm. on Pure and Appl. Math.,1988,41(7):909-996
[57]Mallat S. Multifrequency channel decomposition of images and wavelets. IEEE Trans on ASSP.1989,37(12):2091-2110
[58]Coifman P R, Wickerhauser M V. Entropy based algorithms for best basis selection. IEEE Trans on Inform Theory.1992,38(2):713-718
[59]Coifman P R, Wicerhauser M V. Adapted waveform analysis as a tool for modeling feature extraction and denoising. Optical Engineering.1994,33(7):2170-2174
[60]Goodman T N T, Lee S L, Tang W S. Wavelet wandering subspaces. Trans. Amer. Math. Soc.,1993,338:639-654
[61]Goodman T N T, Lee S L. Wavelets of multiplicity Trans. Amer. Math. Soc.,1994,342:307-324
[62]Sweldens W. The lifting scheme:A custom-design construction of biothogonal wavelets. App. Comut. Hamon. Appl,1996,3(2):186-200
[63]Sweldens W. The lifting scheme:A construction of second generation wavelets. SIAM J. Math. Anal,1997,29(2):511-546
[64]Donovan G, Geronimo J S, Hardin D. Construction of orthogonal wavelets using fractal interpolation function. SIAM J Math. Anal,1996,27:1158-1192P
[65]Lewis A S, Knowles G.. Image compression using the 2-D wavelet transform. IEEE Trans on Image Proc,1992,1(4):244-250
[66]Mallat S. A Wavelet Tour of Signal Processing.2nd. New York:Academic, 1999:244-250
[67]Do M N. Directional multiresolution image representations. Switzerland:School Comput. Commun. Sci. of Swiss Fed. Inst. Technol,2001:33-37
[68]Do M N, Vetterli M. The contourlet transform:an efficient directional multiresolution image representation. IEEE Transactions on Image Processing.2005,14(12):2091-2106
[69]Vetterli M, Kovacevic J. Wavelets and subband coding. Frentice Hall, Englewood Cliffs, NJ,1995:89-102
[70]Donoho D L, Vetterli M, DeVore R, Daubechies. Data compression and harmonic analysis. IEEE Trans. Inf. Theory,1998,44(6):2435-2476
[71]Pennec E L, Mallat S. Image compression with geometrical wavelets. Proc. of ICIP' 2000,2000:661-664
[72]Candes E J. Ridgelets:Theory and applications. USA:Statistics of Stanford Univ,1998:55-63
[73]Candes E J. Harmonic analysis of neural networks. Applied and Computational Harmonic Analysis,1999,6:197-218
[74]Candes E, Donoho D L. Curvelets, Tech. Rep. Stanford Univ., Stanford, CA, 1999:20-43
[75]Donoho D L, Duncan M R. Digital curvelet transform:strategy, implementation and experiments. Proc. SPIE,2000:12-29
[76]Freeman W T, Adelson E H. Steerable filters for early vision, image analysis and wavelet decomposition. Proceedings of 3rd International Conference on Compute Vision,1990:406-415
[77]Freeman W T, Adelson E H. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence.1991,13(9):891-906
[78]Y. Lu, M. N. Do.3-D directional filter banks and surfacelets, Invited Paper, Proc. of SPIE conference on Wavelet Applications in Signal and Image Processing XI, San Diego, USA, August 2005:591-601
[79]尚政国.多分辨率分析及其在图像处理中的应用研究.哈尔滨工程大学博士论文.2008.3
[80]汤春瑞.水下目标声图像多分辨率分析及识别研究.哈尔滨工程大学博士论文.2009.10
[81]L. Atallah and P.J. Probert Smith. Automatic seabed classification by the analysis of sidescan sonar and bathymetric imagery. IEE Proc.-Radar Sonar Navig., Vol.151, No.5, October 2004:327-336
[82]E. Dura, J. Bell and D. Lane. Reconstruction of textured seafloors from side-scan sonar images. IEE Proc.-Radar Sonar Navig., Vol.151, No.2, April 2004:114-126
[83]D.E. Clark and J. Bell. Bayesian multiple target tracking in forward scan sonar images using the PHD filter. IEE Proc.-Radar Sonar Navig., Vol.152, No.5, October 2005:327-334
[84]K. Kim, N. Neretti and N. Intrator. Mosaicing of acoustic camera images. IEE Proc.-Radar Sonar Navig., Vol.152, No.4, August 2005:263-270
[85]M.J. Wilson and R. McHugh. Harmonic array design:technique for efficient non-periodic array optimisation in digital sonar beamforming. IEE Proc.-Radar Sonar Navig., Vol.153, No.1, February 2006:63-68
[86]郭海涛,杨志民,田坦,戴愚志.海底小目标声呐图像一维最大熵分割的改进方法.海洋学报.2007,29(4)：152-155
[87]王兴梅.水下声呐图像目标分割方法的研究及应用.哈尔滨工程大学硕士论文.2008
[88]胡玉薇.基于分形理论的水声图像分割与识别.哈尔滨工程大学硕士论文.2009
[89]叶秀芬,王兴梅,张哲会,方超.改进MRF参数模型的声呐图像分割方法.哈尔滨工程大学学报.2009,30(7)：768-774
[90]卞红雨,刘翠.基于修正二维熵的水声图像分割.计算机工程.2010.36(14)：193-195
[91]Candes E J. Monoscale Ridgelets for the representation of images with edges. Tech. Report, Department of Statistics, Stanford University,1999:61-92
[92]Candes E J, Donoho D L. Curvelet:a surprisingly effective nonadaptive representation for objects with edges. C Rabut A Cohen and L L Schumaker Curves and Surfaces, Nashville, Vanderbilt University Press,2000:105-120
[93]Ramin E, Hayder R. Image denoising using translation invariant contourlet transform. Proceedings of 2005 IEEE International Conference on Acoustics, Speech and Signal Processing,2005:557-560
[94]Do M N, Vetterli M. The contourlet transform:an efficient directional multiresolution image representation. IEEE Trans Im Proc.2005,14:2091-2106
[95]韩晓微,范立南,李浚圣,徐心和.一种基于脉冲噪声检测的图像均值滤波方法.北方交通大学学报.2004,27：102-104页
[96]Xu X Y, Miller E L. Adaptive two-pass median filter to remove impulsive noise. Digital Object Identifier.2002,1:1808-1811
[97]Prasanthi R, Anuradha V, Sahoo S K, Shekhar C. Multiplier less FFT processor architecture for signal and image processing. Intelligent Sensing and Information Processing, Proceedings of 2005 International Conference on.2005:326-330
[98]Tammana G A, Zheng Y F. Image denoising using Multi-Resolution coefficient support based empirical Wiener filtering. Image Processing,2006 IEEE International Conference on.2006:2613-2616
[99]尚政国,赵春晖,孙岩.基于FRIT循环抽样声纳图像去噪新方法,计算机工程与应用,2007.43(10)：1-4页
[100]张海澜,徐文,朱维庆.声图像统计特性的研究.声学学报,1991,16(5)：381-387页
[101]Cunha A L, Zhou J, Do M N. Nonsubsampled contourilet transform:filter design and applications in denoising.Image Processing, IEEE International Conference.2005,1:1 749-1752
[102]Talib M.A., Ibrahim M T, Tauseef S. Slope hold image interpolation using fan filters. Emerging Technologies, Proceedings of the IEEE Symposium.2005:18-20
[103]Marpe Detlev, Cycon Hans L., Zander Gunther, et al. Context-based Denoising of Images Using Iterative Wavelet Thresholding[A], Visual Communications and Image Processing[C]. San Jose, CA, United States 2002:907-914.
[104]吴一全；吴加明；占必超；一种可有效分割小目标图像的阈值选取方法.兵工学报.2011,32(4)：169-475
[105]邓廷权；王占江；汪培培；水下目标动态阈值检测的区间值模糊集熵方法.哈尔滨工程大学学报.2011,32(2)：246-250
[106]吴一全；张晓杰；吴诗婳；基于混沌弹性粒子群优化与基于分解的二维交叉熵阈值分割.上海交通大学学报,2011,45(3)：301-307
[107]包立君；刘宛予；浦昭邦；融合图像局部能量和梯度的水平集分割方法.哈尔滨工业大学学报.2011,43(3)：44-48
[108]杨名宇；丁欢；赵博；张文生；结合邻域信息的Chan-Vese模型图像分割.计算机辅助设计与图形学学报.2011,23(3)：413-417
[109]钟桦；焦李成；侯鹏；基于非下采样Contourlet变换的视网膜分割.计算机学报.2011,34(3)：574-582
[110]贾亚飞；赵凤军；禹卫东；艾加秋；基于扩散方程和MRF的SAR图像分割.电子与信息学报.2011,33(2)：363-368
[111]宋晓峰；王爽；刘芳；基于区域MRF和贝叶斯置信传播的SAR图像分割.电子学报.2010,38(12)：2810-2815
[112]王士龙；徐玉如；万磊；唐旭东；一种基于信息熵约束的快速FCM聚类水下图像分割算法.2010,37(12)：243-246转286
[113]李昱川；田铮；刘向增；徐海霞.SAR图像多尺度配准的小波域最小分割模型方法.电子学报.2010,38(9)：2122-2127.
[114]温沛涵；周建；郑力.基于空间可信性聚类的图像分割算法.清华大学学报(自然科学版).2010,50(8)：1316-1320
[115]朱嵬鹏；王士同；基于空间模式聚类的彩色图像分割.计算机工程与应用.2009,45(34)：161-163
[116]李艳灵；沈轶.基于KNN的均值漂移图像分割算法.华中科技大学学报(自然科学版).2009,37(10)：68-71
[117]柴振华；罗宏文；苗闯；马驷良；一种保持拓扑结构的隐式活动轮廓图像分割方法.吉林大学学报(理学版).2009,47(5)：981-986
[118]Gonzalez R C, Woods R E. Digital image Processing (2nd edition). Beijing: Publishing House of Electronics Industry.2003:20-76
[119]Zhao F W, DeSilva S. Use of the Laplacian of Gaussian operator in prostate ultrasound image processing. Engineering in Medicine and Biology Society, Proceedings of the 20th Annual International Conference of the IEEE.1998,2:812-815
[120]Buxton B.Early image processing structural techniques motivated by human visual response.Guildford, England:University of Surrey,1984:25-57
[121]Zhang Y, Rockett P. The Bayesian Operating Point of the Canny Edge Detector. Image Processing, IEEE Transactions.2006,15 (11):3409-3416
[122]Perona P, Malik J. Scale2space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell,1990,12 (7):629-639.
[123]Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision. Washington,DC,USA:IEEE Computer Society,1998.839-846.
[124]黄沛杰,朱立华,刘学慧等.针对实时视觉通信的图像序列自动提炼.电子学报2009.4月37卷：42-50
[125]Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision. International Joint Conference on Artificial Intelligence. Morgan Kaufmann,1981:674-679.
[126]逯新华；史忠科；基于动态模板匹配的空中运动目标检测与跟踪控制.电子测量与仪器学报.2010,24(10)：935-941
[127]聂烜；陈怀民；朱怡安；一种用于目标检测的复合差分法.西北工业大学学报.2009,27(1)：88-92
[128]雷波；李清泉；复杂交通场景中车辆视频检测的背景提取与更新.武汉大学学报(信息科学版).2009,34(8)：906-909
[129]李劲菊；朱青；王耀南；一种复杂背景下运动目标检测与跟踪方法.仪器仪表学报.2010,31(10)：2242-2247
[130]孙承志；熊田忠；吉顺平；张家海；基于差分的光流法在目标检测跟踪中的应用.机床与液压.2010,38(14)：59-62
[131]盛平；张净；倪冬玮；刘晓梅；基于单运动目标的PTZ检测跟踪系统设计.江苏大学学报(自然科学版).2010,31(6)：710-715
[132]陈功；周荷琴；严捷丰；采用UKF建模的实时背景提取和运动阴影检测.中国图象图形学报.2009,14(5)：931-937
[133]王鲲鹏；杨夏；张恒；用梯度LOG算子实现小目标实时跟踪.光电工程.2009,36(9)：23-28
[134]梁锡宁；杨刚；余学才.一种动态模板匹配的卡尔曼滤波跟踪方法.光电工程.2010,37(10)：29-33
[135]尚秀芹；宋红军；程增菊；赵秉吉；非均匀环境中的分布目标自适应检测.电子与信息学报.2011,33(3)：696-700
[136]刘国成；王永骥；一种基于改进粒子滤波的多目标跟踪算法.控制与决策.2009,24(2)：317-320
[137]韦琦；吴京辉；基于独立分量分析的运动目标精确检测.哈尔滨理工大学学报.2010.15(5)：40-44
[138]逯新华；史忠科；基于动态模板匹配的空中运动目标检测与跟踪控制.电子测量与仪器学报.2010,24(10)：935-941
[139]赵宏伟；冯嘉；臧雪柏；宋波涛；一种实用的运动目标检测和跟踪算法.吉林大学学报(工学版).2009,39(2)：386-390
[140]柏柯嘉；刘伟铭；模糊马尔可夫随机场理论在阴影检测中的应用.中国图象图形学报.2010,15(3)：409-416
[141]刘小军；杨杰；刘惠；沈红斌；基于主成份分析的仿射不变特征图像匹配方法.系统 仿真学报.2008,24(4)：977-980
[142]孙瑾；顾宏斌；秦小麟；周娜；一种鲁棒型Hausdorff距离图像匹配方法.中国图象图形学报.2008,13(4)：761-767
[143]葛馨；刘渊；基于粒子预测的自适应图像匹配目标跟踪算法.计算机工程与设计.2009,30(24)：5673-5675
[144]魏志强；黄磊；纪筱鹏；基于点特征的序列图像匹配方法研究.中国图象图形学报.2009,14(3)：525-530
[145]韩冰；王永明；基于一种快速归一化积相关算法的图像匹配研究.兵工学报.2010,31(2)：160-165
[146]殷松峰；王一程；曹良才；金国藩；凌永顺；基于快速傅里叶变换和积分图的快速相关匹配.光子学报.2010,39(12)：2246-2250
[147]杨猛；潘泉；张绍武；朱英；赵春晖；程咏梅；基于定量定性互信息的多层次特征图像匹配算法.中国图象图形学报.2010,15(9)：1376-1383
[148]陈沈轶；钱徽；吴铮；潘莉莉；朱淼良；基于图分割与Hausdorff距离的多分辨率影像匹配.中国图象图形学报.2008,13(6)：1185-1190
[149]云挺；肖亮；吴慧中；基于运动边界和能量函数最优的图像匹配算法.系统仿真学报.2009,21(4)：987-992
[150]马婧；徐守时；陈克.基于Radon变换的图像角点角度提取算法.光电工程.2009,36(2)：126-131
[151]郝燕玲；王众；基于SNN核的景象匹配算法.自动化学报.2008,34(12)：1475-1482
[152]王云新；刘铁根；江俊峰；张忠传；周苏晋.基于局部SIFT分析的手背静脉识别.光电子.激光.2009,20(5)：681-684
[153]龚卫国；童玉娟；李正浩.各向异性非刚性形变局部不变特征研究.仪器仪表学报.2010,31(1)：99-104
[154]夏瑜；吴小俊.基于MCD和局部线性高斯模型的视频跟踪粒子滤波算法.中国图象图形学报.2009,14(11)：2224-2229
[155]陈宗海；方伟；陈会勇；王智灵.基于分布匹配的主动轮廓模型及其图像分割算法.吉林大学学报(工学版).2008,38(6)：1441-1446
[2]邓承志,汪胜前,钟华,刘祝华,邹道文.基于Contourlet变换的图像去噪算法.电视技术.2004,10：21-22,42页
[3]贾建,焦李成.数字脊波变换的实现与一种改进方法.计算机研究与发展.2006,43(1)：115-119页
[4]王文波,羿旭明,费浦生.基于曲波系数相关性的去噪算法.光电子激光.2006,17(12)：1519-1523页
[5]尚政国,赵春晖,刘金梅.多分辨率域HMT图像去噪增强算法.哈尔滨工程大学学报.2007,28(6)：981-986页
[6]侯一民,郭雷.一种基于马尔可夫随机场的SAR图像分割新方法.电子与信息学报,2007,29(5)：1069-1072页
[7]劳丽.聚类算法研究及在图像分割中的应用.华南理工大学博士论文,2006.
[8]戴剑彬,张大力.图像分析中的松弛标记法.中国图象图形学报,1998,3(2)：97-99.
[9]汪海洋,潘德炉,夏德深.二维Otsu自适应阈值选取算法的快速实现.自动化学报,2007,33(9)：968-971.
[10]Q Yu,A Clausi. IRGS:Image segmentation using edge penalties and region growing. IEEE transactions on pattern analysis and Machine Intelligence,2008.30(12): 2126-2139.
[11]Koss E J, Newman D F, Johnson T K, et al. Abdominal organ segmentation using texture transforms and a hopfield neural network. IEEE Transactions on medical imaging,1999,18(7):640-648.
[12]Sridhar B, Phatak A, Chatterji G. Scene segmentation of natural images using texture measures and back propagation [C]//Artificial Neural Net works. NASA, Tech. Memo. Brighton, UK,1993:200-204.
[13]王兆虎,刘芳,焦李成.一种基于视觉特性的遥感图像分割.计算机学报,2005,28(10)：1686-1691.
[14]Minh N Do, Martin Vetterli. The contourlet transform:an efficient directional multiresolution image representation. IEEE Trans. Image Proc.,2005,14(2):2091-2106.
[15]GaoHa,i SiuWan-Chin, Hou Chao-Huan. Improved techniques for automatic image segmentation. IEEE Transactions on Circuits and Systems forVideoTechnology,2001, 11(12):1273-1280.
[16]Salembier P, PardasM. Hierarchical morphological segmentation for image sequence coding. IEEE Transactions on Image Processing,1994,3(5):639-651.
[17]R. Callaghan and D. Bul,l"Combined morphological-spectral unsupervised image segmentation,"[J]. IEEE Trans. Image Process,2005,14(1):49-62.
[18]M.N.Do,"Directionalmultiresolution image representations,"Ph.D. dissertation, SchoolCompu.t Commun. Sc.i. SwissFed. Ins.t Techno.l, Lausanne, Switzerland,2001.
[19]M.Sammouda, R.Sammouda, N.Niki,K. Mukai, Segmentation and analysis of liver cancer pathological color image based on artificial neural networks, Proceedings of IEEE 1999 International Conference on Image Processing,1999,Kobe,Japan,Vol.3,pp:392-396.
[20]R.Sammouda, M.Sammouda, Improving the performance of Hopfield neural network to segment pathological liver color images, International Congress Series,Vol.1256, pp:232-239,2003.
[21]S.H.Ong,N.C.Yeo,K.H.Lee,Y.V.Venkatesh,D.M.Cao,Segmentation of color images using a two-stage self-organizing network,Image and Vision Computing,Vol.20,No.4, pp:279-289,2002.
[22]P.Lescure,V.M.Yedid,H.Dupoisot,G.Stamon,Color segmentation on biological mi-croscope images,Proceeding of SPIE,Application of Artificial Neural Networks in Image Processing Ⅳ,1999,San Jose, California, USA, Vol.3647,pp:182-193.
[23]吴晓岩,RPCL算法在自动化图像分割中的应用,北京大学硕士论文,2003.
[24]王亮,胡卫明,谭铁牛.人运动的视觉分析综述.计算机学报,2002,25(3)
[25]代科学,李国辉,徐丹,袁见.监控视频运动目标检测减背景技术的研究现状和展望.中国图像图形学报,2006,11(7)
[26]A Lipton,H Fujiyoshi,R Patil. Moving target classification and tracking from real-time videl. IEEE Workshop on Application of Computer Vision,Princeton,NJ,1998.129-136
[27]L Wang, W Hu, T Tan. Recent developments in human motion analysis. In proceedings of International Conference on Pattern Recogniting, France,2003,36(3):585-601
[28]R Collions, A system gor cideo surceillance and monitoring VASM final repert.CMU-RI-TR-00-12,Robotic Institute Carngie Mellon University,2000
[29]A Verris, S Uras, E De Micheli. Motion segmentation from Optical FlowIn proceedings of the 5 th Alvey Vision Conference,1989.209-214
[30]J Barron, D Fleet, S Beauchemin. Perfrmance of Optical Flow Techniques.International Journal of Computer Vision,1994,12(1):42-77
[31]J Heikkila,0 Silven.A real-time system for monitoring of cyclists and pedestrians,In proceedings of 2th IEEE Workshop on Visual Surveillance,Colorado,1999.74-81
[32]R Cutler, L Davis. View-Based Detection and Analysis os Periodic Motion. In proceedings of Intenational Conference on Pattern Recognition Australia,1998:495-500
[33]R Cucchiara, M Picacardi, A Prati. Detecting moving objects, ghosts and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1337-1342
[34]Lin Hongwen, Tu Dan,Li Guo-hui. Moving objects detection method based on statistical background model. Computer Enginering,2003,29(16):97-99
[35]Andrea Canallaro, Olivier Steiger, Touradj Ebrahimi. Multple Video Object Tracking in Complex Scenes. IEEE Multinedia 02,France,Juan-les-Pins,pp:523-532,2002
[36]Bramberger M,Quaritseh M, Winkler T,et al. Integrating Multi-Camera Tracking into a Dynamic Task Allocation System for Smart Cameras.IEEE Conference on Advanced Videl and Signal Based Surveillance,2005:474-479
[37]Cipolla R. Pentland A. Computer Vision for Human-Machine Iteration. Cambridge University Press,1998
[38]Blake A,Isard M. Active Contours.Springer-Verlag,1998
[39]吴健康,肖玉.计算机视觉基本理论和方法,合肥：中国科技大学出版社
[40]Hu W M, Tan T N,Wang L, et al. A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions on SysTEM Man and Cybernetics,2004,34(3):334-351
[41]KELL Y P H, KATKERE A, KURAMURA D Y, et al. An architecture for multiple perspective interactive video//Proc of the 3nd ACM International Conference on Multimedia. San Francisco:[s.n.],1995:2012212.201-202
[42]ZHOU Quming, A GGARWAL J K. Object t racking in an outdoor environment using fusion of features and cameras. Image and Vision Computing,2006,24 (11): 124421255.1244-1255
[43]XU M, ORWELL J, LOWEYL, et al. Architectureand algorithms for t racking football players with mul2 tiple cameras. Image and Signal Processing,2005,152 (2):2322241.232-241
[44]JAVERD O, RASHEED Z, SHAFIQUE K, et al. Tracking across multiple cameras with disjoint views//Proceedings of IEEE International Conference on Computer Vision. Nice:[s. n],2003:1652171.165-171
[45]HU Wei-Ming et al. Principal axis-based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28(4):663-671
[46]Chang T H, Gong S, Ong E J. Tracking multiple people under occlusion using multiple cameras//Proceedings of the British Machine Vision Conference. Bristol, UK,2000: 566-575
[47]Krumm J et al. Multi-camera multi-person tracking for easy living//Proceedings of the IEEE International Workshop Visual Surveillance. Dublin, Ireland,2000:3-10
[48]Mittal A, Davis L S. M2Tracker:A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo//Proceedings of the European Conference on Computer Vision. Copenhagen, Denmarky,2002:18-36
[49]Bollobas B, Thomason A G. Random Graphics of Small Order. North-Holland, Amsterdam,1985:47-97
[50]Avidan S, Moses Y, Moses Y. Centralized and distributed multi-view correspondence. International Journal on Computer Vision,2007,71(1):49-69
[51]Tsutsui H, Miura J, Shirai Y. Optical flow-based person tracking by multiple cameras//Proceedings of the IEEE Conference on Multisensor Fusion and Integration in Intelligent Systems. Baden-Baden, Germany,2001:91-96
[52]Orwell J, Remagnino P, Jones G A. Multiple camera color tracking//Proceedings of the IEEE International Workshop Visual Surveillance. Fort Collins, USA,1999:14-24
[53]Cai Q, Aggarwal J K. Tracking human motion in structured environments using a distributed-camera system. IEEE Transactions on Pattern Recognition and Machine Intelligence,1999,21(11):1241-1247
[54]Lowe David G. Object recognition from local scale-invariant features//Proceedings of the International Conference on Computer Vision. Corfu, Greece,1999:1150-1157
[55]Brown M, Lowe D. Recognizing panoramas//Proceedings of the International Conference on Computer Vision. Nice, France,2003:1218-1225
[56]Daubechies I. Orthonormal bases of compactly supported wavelets. Comm. on Pure and Appl. Math.,1988,41(7):909-996
[57]Mallat S. Multifrequency channel decomposition of images and wavelets. IEEE Trans on ASSP.1989,37(12):2091-2110
[58]Coifman P R, Wickerhauser M V. Entropy based algorithms for best basis selection. IEEE Trans on Inform Theory.1992,38(2):713-718
[59]Coifman P R, Wicerhauser M V. Adapted waveform analysis as a tool for modeling feature extraction and denoising. Optical Engineering.1994,33(7):2170-2174
[60]Goodman T N T, Lee S L, Tang W S. Wavelet wandering subspaces. Trans. Amer. Math. Soc.,1993,338:639-654
[61]Goodman T N T, Lee S L. Wavelets of multiplicity Trans. Amer. Math. Soc.,1994,342:307-324
[62]Sweldens W. The lifting scheme:A custom-design construction of biothogonal wavelets. App. Comut. Hamon. Appl,1996,3(2):186-200
[63]Sweldens W. The lifting scheme:A construction of second generation wavelets. SIAM J. Math. Anal,1997,29(2):511-546
[64]Donovan G, Geronimo J S, Hardin D. Construction of orthogonal wavelets using fractal interpolation function. SIAM J Math. Anal,1996,27:1158-1192P
[65]Lewis A S, Knowles G.. Image compression using the 2-D wavelet transform. IEEE Trans on Image Proc,1992,1(4):244-250
[66]Mallat S. A Wavelet Tour of Signal Processing.2nd. New York:Academic, 1999:244-250
[67]Do M N. Directional multiresolution image representations. Switzerland:School Comput. Commun. Sci. of Swiss Fed. Inst. Technol,2001:33-37
[68]Do M N, Vetterli M. The contourlet transform:an efficient directional multiresolution image representation. IEEE Transactions on Image Processing.2005,14(12):2091-2106
[69]Vetterli M, Kovacevic J. Wavelets and subband coding. Frentice Hall, Englewood Cliffs, NJ,1995:89-102
[70]Donoho D L, Vetterli M, DeVore R, Daubechies. Data compression and harmonic analysis. IEEE Trans. Inf. Theory,1998,44(6):2435-2476
[71]Pennec E L, Mallat S. Image compression with geometrical wavelets. Proc. of ICIP' 2000,2000:661-664
[72]Candes E J. Ridgelets:Theory and applications. USA:Statistics of Stanford Univ,1998:55-63
[73]Candes E J. Harmonic analysis of neural networks. Applied and Computational Harmonic Analysis,1999,6:197-218
[74]Candes E, Donoho D L. Curvelets, Tech. Rep. Stanford Univ., Stanford, CA, 1999:20-43
[75]Donoho D L, Duncan M R. Digital curvelet transform:strategy, implementation and experiments. Proc. SPIE,2000:12-29
[76]Freeman W T, Adelson E H. Steerable filters for early vision, image analysis and wavelet decomposition. Proceedings of 3rd International Conference on Compute Vision,1990:406-415
[77]Freeman W T, Adelson E H. The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence.1991,13(9):891-906
[78]Y. Lu, M. N. Do.3-D directional filter banks and surfacelets, Invited Paper, Proc. of SPIE conference on Wavelet Applications in Signal and Image Processing XI, San Diego, USA, August 2005:591-601
[79]尚政国.多分辨率分析及其在图像处理中的应用研究.哈尔滨工程大学博士论文.2008.3
[80]汤春瑞.水下目标声图像多分辨率分析及识别研究.哈尔滨工程大学博士论文.2009.10
[81]L. Atallah and P.J. Probert Smith. Automatic seabed classification by the analysis of sidescan sonar and bathymetric imagery. IEE Proc.-Radar Sonar Navig., Vol.151, No.5, October 2004:327-336
[82]E. Dura, J. Bell and D. Lane. Reconstruction of textured seafloors from side-scan sonar images. IEE Proc.-Radar Sonar Navig., Vol.151, No.2, April 2004:114-126
[83]D.E. Clark and J. Bell. Bayesian multiple target tracking in forward scan sonar images using the PHD filter. IEE Proc.-Radar Sonar Navig., Vol.152, No.5, October 2005:327-334
[84]K. Kim, N. Neretti and N. Intrator. Mosaicing of acoustic camera images. IEE Proc.-Radar Sonar Navig., Vol.152, No.4, August 2005:263-270
[85]M.J. Wilson and R. McHugh. Harmonic array design:technique for efficient non-periodic array optimisation in digital sonar beamforming. IEE Proc.-Radar Sonar Navig., Vol.153, No.1, February 2006:63-68
[86]郭海涛,杨志民,田坦,戴愚志.海底小目标声呐图像一维最大熵分割的改进方法.海洋学报.2007,29(4)：152-155
[87]王兴梅.水下声呐图像目标分割方法的研究及应用.哈尔滨工程大学硕士论文.2008
[88]胡玉薇.基于分形理论的水声图像分割与识别.哈尔滨工程大学硕士论文.2009
[89]叶秀芬,王兴梅,张哲会,方超.改进MRF参数模型的声呐图像分割方法.哈尔滨工程大学学报.2009,30(7)：768-774
[90]卞红雨,刘翠.基于修正二维熵的水声图像分割.计算机工程.2010.36(14)：193-195
[91]Candes E J. Monoscale Ridgelets for the representation of images with edges. Tech. Report, Department of Statistics, Stanford University,1999:61-92
[92]Candes E J, Donoho D L. Curvelet:a surprisingly effective nonadaptive representation for objects with edges. C Rabut A Cohen and L L Schumaker Curves and Surfaces, Nashville, Vanderbilt University Press,2000:105-120
[93]Ramin E, Hayder R. Image denoising using translation invariant contourlet transform. Proceedings of 2005 IEEE International Conference on Acoustics, Speech and Signal Processing,2005:557-560
[94]Do M N, Vetterli M. The contourlet transform:an efficient directional multiresolution image representation. IEEE Trans Im Proc.2005,14:2091-2106
[95]韩晓微,范立南,李浚圣,徐心和.一种基于脉冲噪声检测的图像均值滤波方法.北方交通大学学报.2004,27：102-104页
[96]Xu X Y, Miller E L. Adaptive two-pass median filter to remove impulsive noise. Digital Object Identifier.2002,1:1808-1811
[97]Prasanthi R, Anuradha V, Sahoo S K, Shekhar C. Multiplier less FFT processor architecture for signal and image processing. Intelligent Sensing and Information Processing, Proceedings of 2005 International Conference on.2005:326-330
[98]Tammana G A, Zheng Y F. Image denoising using Multi-Resolution coefficient support based empirical Wiener filtering. Image Processing,2006 IEEE International Conference on.2006:2613-2616
[99]尚政国,赵春晖,孙岩.基于FRIT循环抽样声纳图像去噪新方法,计算机工程与应用,2007.43(10)：1-4页
[100]张海澜,徐文,朱维庆.声图像统计特性的研究.声学学报,1991,16(5)：381-387页
[101]Cunha A L, Zhou J, Do M N. Nonsubsampled contourilet transform:filter design and applications in denoising.Image Processing, IEEE International Conference.2005,1:1 749-1752
[102]Talib M.A., Ibrahim M T, Tauseef S. Slope hold image interpolation using fan filters. Emerging Technologies, Proceedings of the IEEE Symposium.2005:18-20
[103]Marpe Detlev, Cycon Hans L., Zander Gunther, et al. Context-based Denoising of Images Using Iterative Wavelet Thresholding[A], Visual Communications and Image Processing[C]. San Jose, CA, United States 2002:907-914.
[104]吴一全；吴加明；占必超；一种可有效分割小目标图像的阈值选取方法.兵工学报.2011,32(4)：169-475
[105]邓廷权；王占江；汪培培；水下目标动态阈值检测的区间值模糊集熵方法.哈尔滨工程大学学报.2011,32(2)：246-250
[106]吴一全；张晓杰；吴诗婳；基于混沌弹性粒子群优化与基于分解的二维交叉熵阈值分割.上海交通大学学报,2011,45(3)：301-307
[107]包立君；刘宛予；浦昭邦；融合图像局部能量和梯度的水平集分割方法.哈尔滨工业大学学报.2011,43(3)：44-48
[108]杨名宇；丁欢；赵博；张文生；结合邻域信息的Chan-Vese模型图像分割.计算机辅助设计与图形学学报.2011,23(3)：413-417
[109]钟桦；焦李成；侯鹏；基于非下采样Contourlet变换的视网膜分割.计算机学报.2011,34(3)：574-582
[110]贾亚飞；赵凤军；禹卫东；艾加秋；基于扩散方程和MRF的SAR图像分割.电子与信息学报.2011,33(2)：363-368
[111]宋晓峰；王爽；刘芳；基于区域MRF和贝叶斯置信传播的SAR图像分割.电子学报.2010,38(12)：2810-2815
[112]王士龙；徐玉如；万磊；唐旭东；一种基于信息熵约束的快速FCM聚类水下图像分割算法.2010,37(12)：243-246转286
[113]李昱川；田铮；刘向增；徐海霞.SAR图像多尺度配准的小波域最小分割模型方法.电子学报.2010,38(9)：2122-2127.
[114]温沛涵；周建；郑力.基于空间可信性聚类的图像分割算法.清华大学学报(自然科学版).2010,50(8)：1316-1320
[115]朱嵬鹏；王士同；基于空间模式聚类的彩色图像分割.计算机工程与应用.2009,45(34)：161-163
[116]李艳灵；沈轶.基于KNN的均值漂移图像分割算法.华中科技大学学报(自然科学版).2009,37(10)：68-71
[117]柴振华；罗宏文；苗闯；马驷良；一种保持拓扑结构的隐式活动轮廓图像分割方法.吉林大学学报(理学版).2009,47(5)：981-986
[118]Gonzalez R C, Woods R E. Digital image Processing (2nd edition). Beijing: Publishing House of Electronics Industry.2003:20-76
[119]Zhao F W, DeSilva S. Use of the Laplacian of Gaussian operator in prostate ultrasound image processing. Engineering in Medicine and Biology Society, Proceedings of the 20th Annual International Conference of the IEEE.1998,2:812-815
[120]Buxton B.Early image processing structural techniques motivated by human visual response.Guildford, England:University of Surrey,1984:25-57
[121]Zhang Y, Rockett P. The Bayesian Operating Point of the Canny Edge Detector. Image Processing, IEEE Transactions.2006,15 (11):3409-3416
[122]Perona P, Malik J. Scale2space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell,1990,12 (7):629-639.
[123]Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Proceedings of the Sixth International Conference on Computer Vision. Washington,DC,USA:IEEE Computer Society,1998.839-846.
[124]黄沛杰,朱立华,刘学慧等.针对实时视觉通信的图像序列自动提炼.电子学报2009.4月37卷：42-50
[125]Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision. International Joint Conference on Artificial Intelligence. Morgan Kaufmann,1981:674-679.
[126]逯新华；史忠科；基于动态模板匹配的空中运动目标检测与跟踪控制.电子测量与仪器学报.2010,24(10)：935-941
[127]聂烜；陈怀民；朱怡安；一种用于目标检测的复合差分法.西北工业大学学报.2009,27(1)：88-92
[128]雷波；李清泉；复杂交通场景中车辆视频检测的背景提取与更新.武汉大学学报(信息科学版).2009,34(8)：906-909
[129]李劲菊；朱青；王耀南；一种复杂背景下运动目标检测与跟踪方法.仪器仪表学报.2010,31(10)：2242-2247
[130]孙承志；熊田忠；吉顺平；张家海；基于差分的光流法在目标检测跟踪中的应用.机床与液压.2010,38(14)：59-62
[131]盛平；张净；倪冬玮；刘晓梅；基于单运动目标的PTZ检测跟踪系统设计.江苏大学学报(自然科学版).2010,31(6)：710-715
[132]陈功；周荷琴；严捷丰；采用UKF建模的实时背景提取和运动阴影检测.中国图象图形学报.2009,14(5)：931-937
[133]王鲲鹏；杨夏；张恒；用梯度LOG算子实现小目标实时跟踪.光电工程.2009,36(9)：23-28
[134]梁锡宁；杨刚；余学才.一种动态模板匹配的卡尔曼滤波跟踪方法.光电工程.2010,37(10)：29-33
[135]尚秀芹；宋红军；程增菊；赵秉吉；非均匀环境中的分布目标自适应检测.电子与信息学报.2011,33(3)：696-700
[136]刘国成；王永骥；一种基于改进粒子滤波的多目标跟踪算法.控制与决策.2009,24(2)：317-320
[137]韦琦；吴京辉；基于独立分量分析的运动目标精确检测.哈尔滨理工大学学报.2010.15(5)：40-44
[138]逯新华；史忠科；基于动态模板匹配的空中运动目标检测与跟踪控制.电子测量与仪器学报.2010,24(10)：935-941
[139]赵宏伟；冯嘉；臧雪柏；宋波涛；一种实用的运动目标检测和跟踪算法.吉林大学学报(工学版).2009,39(2)：386-390
[140]柏柯嘉；刘伟铭；模糊马尔可夫随机场理论在阴影检测中的应用.中国图象图形学报.2010,15(3)：409-416
[141]刘小军；杨杰；刘惠；沈红斌；基于主成份分析的仿射不变特征图像匹配方法.系统 仿真学报.2008,24(4)：977-980
[142]孙瑾；顾宏斌；秦小麟；周娜；一种鲁棒型Hausdorff距离图像匹配方法.中国图象图形学报.2008,13(4)：761-767
[143]葛馨；刘渊；基于粒子预测的自适应图像匹配目标跟踪算法.计算机工程与设计.2009,30(24)：5673-5675
[144]魏志强；黄磊；纪筱鹏；基于点特征的序列图像匹配方法研究.中国图象图形学报.2009,14(3)：525-530
[145]韩冰；王永明；基于一种快速归一化积相关算法的图像匹配研究.兵工学报.2010,31(2)：160-165
[146]殷松峰；王一程；曹良才；金国藩；凌永顺；基于快速傅里叶变换和积分图的快速相关匹配.光子学报.2010,39(12)：2246-2250
[147]杨猛；潘泉；张绍武；朱英；赵春晖；程咏梅；基于定量定性互信息的多层次特征图像匹配算法.中国图象图形学报.2010,15(9)：1376-1383
[148]陈沈轶；钱徽；吴铮；潘莉莉；朱淼良；基于图分割与Hausdorff距离的多分辨率影像匹配.中国图象图形学报.2008,13(6)：1185-1190
[149]云挺；肖亮；吴慧中；基于运动边界和能量函数最优的图像匹配算法.系统仿真学报.2009,21(4)：987-992
[150]马婧；徐守时；陈克.基于Radon变换的图像角点角度提取算法.光电工程.2009,36(2)：126-131
[151]郝燕玲；王众；基于SNN核的景象匹配算法.自动化学报.2008,34(12)：1475-1482
[152]王云新；刘铁根；江俊峰；张忠传；周苏晋.基于局部SIFT分析的手背静脉识别.光电子.激光.2009,20(5)：681-684
[153]龚卫国；童玉娟；李正浩.各向异性非刚性形变局部不变特征研究.仪器仪表学报.2010,31(1)：99-104
[154]夏瑜；吴小俊.基于MCD和局部线性高斯模型的视频跟踪粒子滤波算法.中国图象图形学报.2009,14(11)：2224-2229
[155]陈宗海；方伟；陈会勇；王智灵.基于分布匹配的主动轮廓模型及其图像分割算法.吉林大学学报(工学版).2008,38(6)：1441-1446