堆石混凝土施工管理中视觉信息的处理方法及应用研究
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摘要
堆石混凝土技术作为一种新型的筑坝技术,自2003年提出以来,已经应用于多处工程。该技术对堆石仓面质量和自密实混凝土的状态有严格的要求。目前,堆石仓面质量和自密实混凝土的状态只能通过人工巡视目测的方法进行检测,存在主观性强、精度低等缺点。因此本文尝试对施工现场获得的视觉信息进行分析处理,引入非接触式、客观、便捷的方法用于施工质量管理。
本文以堆石混凝土施工管理为研究对象,对施工管理中的需求进行分析,将现场目标分为运动目标和静止目标,研究和开发了相对应的视觉信息处理算法,并开发了基于视觉信息的堆石混凝土施工管理系统。
堆石混凝土施工管理中的运动目标包括运石车辆、上料料斗、流动的自密实混凝土等,可以通过对其轨迹进行分析处理得到管理所需的信息。在单摄像机静止条件下,施工现场的复杂环境给多目标跟踪带来了一定的困难,主要体现在背景的变化较多以及目标间相互遮挡。本文分别对目标分割、定位和遮挡情况下的目标跟踪方法进行研究,并在此基础上建立了基于高频值的自适应背景更新模型;改进了基于游程递归的连通区域标记算法;开发了基于轨迹连续性判断的遮挡情况下多目标跟踪算法。实验结果表明,整合以上算法可实现对施工现场中多运动目标的实时跟踪,并得到管理所需的信息。
堆石混凝土施工管理中的静止目标包括堆石入仓后仓面上的石块及自密实混凝土浇筑完成后仓面上的裸露石块等,可以通过对目标形态的识别来获得管理所需的信息。本文主要研究堆石仓面上各种石块的粒径信息提取方法。为解决石块间重叠、接触给石块粒径识别带来的困难,总结分析了粘连区域分割方法,应用分水岭算法实现块石粒径的提取,并把堆石图像自动识别的块石粒径分布和人工识别的结果进行比较,验证了本文中石块分割算法的准确性。
本文将上述视觉信息处理方法进行整合,开发了堆石混凝土施工管理系统,将其用于跟踪施工中的料斗,得到自密实混凝土的生产量;用于提取石块粒径信息,通过处理堆石仓面照片自动得到堆石的级配曲线。该系统可用于堆石混凝土施工现场其他目标的管理;也可推广应用到其他工程的施工管理中。
Rock-Filled Concrete (RFC) has been employed in a number of hydraulic engineering as a new type of concrete since it was developed in 2003. RFC construction requires quality control of proper rock size distribution and qualified self-compacting concrete (SCC). By now, the quality control of RFC construction is conducted by traditional methods of collecting data using human judgement with low precision. In this study, a non-contact, objective and convenient system is proposed and developed based on automatical visual information processing methods for improving the quality management on RFC construction sites.
RFC dam construction projects are used for the development of this visual information processing system. The practical demands of construction site management are analyzed, and classified into two parts: multi-moving-targets tracking and static objects detection.
For multi-moving-targets, the track of moving targets such as vehicles, hoppers and SCC material on construction sites are required for management. Using a fixed video camera for image collecting, the main difficulties for multi-moving-targets tracking are the changing background and the presence of occluded targets in the complex environment of the construction site. The existing methods for targets detection, location and tracking under occlusions are analyzed. The adaptive background model based on most frequent value is proposed for targets detection. The connected component labeling algorithm using run recursive method is improved for targets location. The matching method according to judgment of consecutive trajectory is developed for targets tracking. The experimental results show that the moving targets can be real-timely detected、located and tracked precisely using these proposed algorithms.
For static objects detection, the characteristic of objects needed to be detected for quality management is size distribution of rock mass on construction sites. The main difficulty for detection is to segment the overlapped objects. The existing methods for separating overlapped objects are reviewed, and watershed segmentation method is adopted to detect the size of each rock from an image. The experimental results compared with manual detection show that the method is feasible and effective.
The visual information processing system is developed based on the proposed algorithms and methods. This system is applied to track the hopper for evaluating the production of SCC, and to detect the rock size distribution for quality control. In the future, the system can be developed for visual information processing of other targets in Rock-filled concrete construction management, or even for targeting management in other type of construction process.
本文以堆石混凝土施工管理为研究对象,对施工管理中的需求进行分析,将现场目标分为运动目标和静止目标,研究和开发了相对应的视觉信息处理算法,并开发了基于视觉信息的堆石混凝土施工管理系统。
堆石混凝土施工管理中的运动目标包括运石车辆、上料料斗、流动的自密实混凝土等,可以通过对其轨迹进行分析处理得到管理所需的信息。在单摄像机静止条件下,施工现场的复杂环境给多目标跟踪带来了一定的困难,主要体现在背景的变化较多以及目标间相互遮挡。本文分别对目标分割、定位和遮挡情况下的目标跟踪方法进行研究,并在此基础上建立了基于高频值的自适应背景更新模型;改进了基于游程递归的连通区域标记算法;开发了基于轨迹连续性判断的遮挡情况下多目标跟踪算法。实验结果表明,整合以上算法可实现对施工现场中多运动目标的实时跟踪,并得到管理所需的信息。
堆石混凝土施工管理中的静止目标包括堆石入仓后仓面上的石块及自密实混凝土浇筑完成后仓面上的裸露石块等,可以通过对目标形态的识别来获得管理所需的信息。本文主要研究堆石仓面上各种石块的粒径信息提取方法。为解决石块间重叠、接触给石块粒径识别带来的困难,总结分析了粘连区域分割方法,应用分水岭算法实现块石粒径的提取,并把堆石图像自动识别的块石粒径分布和人工识别的结果进行比较,验证了本文中石块分割算法的准确性。
本文将上述视觉信息处理方法进行整合,开发了堆石混凝土施工管理系统,将其用于跟踪施工中的料斗,得到自密实混凝土的生产量;用于提取石块粒径信息,通过处理堆石仓面照片自动得到堆石的级配曲线。该系统可用于堆石混凝土施工现场其他目标的管理;也可推广应用到其他工程的施工管理中。
Rock-Filled Concrete (RFC) has been employed in a number of hydraulic engineering as a new type of concrete since it was developed in 2003. RFC construction requires quality control of proper rock size distribution and qualified self-compacting concrete (SCC). By now, the quality control of RFC construction is conducted by traditional methods of collecting data using human judgement with low precision. In this study, a non-contact, objective and convenient system is proposed and developed based on automatical visual information processing methods for improving the quality management on RFC construction sites.
RFC dam construction projects are used for the development of this visual information processing system. The practical demands of construction site management are analyzed, and classified into two parts: multi-moving-targets tracking and static objects detection.
For multi-moving-targets, the track of moving targets such as vehicles, hoppers and SCC material on construction sites are required for management. Using a fixed video camera for image collecting, the main difficulties for multi-moving-targets tracking are the changing background and the presence of occluded targets in the complex environment of the construction site. The existing methods for targets detection, location and tracking under occlusions are analyzed. The adaptive background model based on most frequent value is proposed for targets detection. The connected component labeling algorithm using run recursive method is improved for targets location. The matching method according to judgment of consecutive trajectory is developed for targets tracking. The experimental results show that the moving targets can be real-timely detected、located and tracked precisely using these proposed algorithms.
For static objects detection, the characteristic of objects needed to be detected for quality management is size distribution of rock mass on construction sites. The main difficulty for detection is to segment the overlapped objects. The existing methods for separating overlapped objects are reviewed, and watershed segmentation method is adopted to detect the size of each rock from an image. The experimental results compared with manual detection show that the method is feasible and effective.
The visual information processing system is developed based on the proposed algorithms and methods. This system is applied to track the hopper for evaluating the production of SCC, and to detect the rock size distribution for quality control. In the future, the system can be developed for visual information processing of other targets in Rock-filled concrete construction management, or even for targeting management in other type of construction process.
引文
[1]金峰,安雪晖.堆石混凝土大坝施工方法:中国,ZL03102074.5.2006-01-25
[2] David A. Forsyth, Jean Ponce. Computer vision: a modern approach.林学訚,王宏等译.北京:电子工业出版社,2004
[3]冈萨雷斯.数字图像处理(第二版).阮秋琦等译.北京:电子工业出版社,2003
[4]章毓晋.图像分割.北京:科学出版社,2001
[5] Alper Yilmaz, Omar Javed, Mubarak Shah. Object Tracking: A Survey. ACM Comput. Surv., 2006, 38(4): 13.1~13.45
[6]黄声亨,曾怀恩. GPS实时监控系统碾压试验的高程精度评定.测绘信息与工程,2004,29(5): 39~40
[7]黄声享,刘经南,吴晓铭. GPS实时监控系统研制及其在堆石坝施工中的初步应用.武汉大学学报(信息科学版),2005,30(9): 13~16
[8]黄声亨,曾怀恩. GPS实时监控系统及其在碾压施工中的应用.测绘工程,2006,15(3): 23~25
[9]吴晓铭.水布垭大坝填筑碾压施工质量监控系统研究.湖北水力发电,2002,44(1): 27~28
[10]吴浩,王乾坤,陈沁,李岩.基于GPS/ GIS集成大坝碾压施工监控平台研究.武汉理工大学学报,2009,31(15): 45~48
[11]王乾坤,陈沁. GPS大坝施工碾压质量实时监控方法.武汉理工大学学报,2009,31(8): 79~82
[12]钟登华,毛寨汉,朱慧蓉.水利水电工程土石方开挖可视化设计方法初探.水利水电技术,2001,32 (12): 32~34
[13]钟登华,宋洋,黄河,郑家祥.基于GIS水电工程施工系统三维动态图形仿真技术.系统仿真学报,2003,15(12): 1766~1770
[14]钟登华,周锐,刘东海.水利水电工程施工系统三维建模与仿真.计算机仿真,2003,20(2): 86~91
[15]钟登华,南春辉,宋洋.水电工程施工进度三维动态可视化方法.天津大学学报,2005,38(4): 322~327
[16]黄少华,张德文,李小帅.虚拟现实技术在水利工程仿真中的应用.人民长江, 2006,37(5): 36~38
[17]黄少华,史那.基于VR与GIS的水利工程视算一体化系统的研制.测绘信息与工程,2007,32(5): 1~2
[18]任庆海,陈海霞.视频监控系统在水利工程中的应用.水利水文自动化,2004(1): 19~22
[19]关晓惠.视频监控技术研究及其在水利信息化中的应用.浙江水利水电专科学校学报,2006,18(1): 13~15
[20]蔡铂.浅谈视频监控系统在施工现场的运用.山西建筑,2008,34(14): 365~366
[21]张玉燕,黄晞淳,武佳枚.浅谈视频监控系统在水利行业中的应用趋势.水利科技,2009(8): 26~29
[22] http://www.it.com.cn/f/market/069/13/324276.htm
[23]秦少杰.赤峰市三座店水利枢纽工程网络及视频监控系统设计.吉林水利,2005(7): 41~42
[24]高春平,姜黎辉,何雄.远程实时视频数字监控系统在防汛抢险中的应用. 2004(4): 58~60
[25]王五平,宋人心,傅翔,罗骐先.用超声波CT探测混凝土内部缺陷.水利水运工程学报,2003(2): 56~60
[26]肖敏芳,李卓球.基于混凝土无损探伤的超声CT图像分析和处理:[硕士学位论文].武汉:武汉理工大学理学院,2006
[27]王宝庭,徐道远,宋玉普.数字图像处理技术在混凝土研究中的应用.河海大学学报,1999,27(4): 60~63
[28]李楠,王晓琴,杨吉新.混凝土外观质量智能评判技术.交通科技,2006,218(5): 97~99
[29]耿飞,钱春香.图像分析技术在混凝土收缩裂缝定量测试与评价中的应用研究.东南大学学报(自然科学版),2003(6): 773~776
[30]耿飞,解建光,钱春香.图像分析技术对混凝土裂缝的定量评价.混凝土,2005,187(5): 78~80
[31]刘清元,谈桥.基于图像处理的混凝土裂缝的检测.武汉理工大学学报,2005,27(4): 69~71
[32]王伟,何小元,杨福俊,尹兰.基于数字光测的表面裂缝宽度图像分析法.测控技术,2006,25(7): 23~25
[33]王雷,黄战华,蔡怀宇,刘正.工程结构构件裂缝图像的处理与宽度测量.计算机测量与控制,2006,14(8): 992~993
[34] Benning Wilhelm, Lange Johannes, Schwermann Raimund, Effkemann Christoph, G?rtz S. Monitoring crack origin and evolution at concrete elements using photogrammetry. In , ISPRS(International Society of Photogrammetry and Remote Sensing), Commission V, 2004:678~683
[35] Lange Johannes, Benning Wilhelm, Siering Karen. Crack detection at concrete construction units from photogrammetric data using image processing procedures. In , ISPRS Mid-term Symposium 2006, Commission VI WG VI/4, 2006
[36]于承新,徐芳,黄桂兰,刘友光.近景摄影测量在钢结构变形监测中的应用.山东建筑工程学院学报,15(4),2000: 1~7
[37]曲哲,陆新征,叶列平,程锦.数字摄影测量在混凝土梁受剪性能研究中的应用.建筑结构学报,27(2),2006: 936~939
[38] Psaltis C., C.Ioannidis. An automatic technique for accurate non-contact structural deformation measurements. IAPRS Volume XXXVI, Part5, Dresden, 2006
[39]张国栋,赵文光,文银平.结构动态位移监测图像处理技术的工程应用.华中科技大学学报,2005(22): 10~12
[40] Tournas L, Tsakiri M, Kattis M. Displacement monitoring at the micron level using digital photogrammetry. 3rd IAG/12th FIG Symposium, Baden, May22-24, 2006
[41]李文炳,冯平,蔡增伸,叶蔚嫦.基于数字图像处理的位移测量.浙江工业大学学报,2004,32(6): 688~691
[42] Ioannis K. Brilakis, Lucio Soibelman, Yoshihisa Shinagawa. Construction site image retrieval based on material cluster recognition. Advanced Engineering Informatics, 2006, 20:443~452
[43] Xiaonan Zhang, Nick Bakis, Timothy C. Lukins, et al. Automating progress measurement of construction projects. Automation in Construction. 2009, 18:294~301
[44] Y.M. Ibrahim, T.C. Lukins, X. Zhang. Towards automated progress assessment of workpackage components in construction projects using computer vision. Advanced Engineering Informatics, 2009, 23:93~103
[45] R.Navon. Automated project performance control of construction projects. Automation in Construction, 2005, 14 (4):467~476.
[46] Teizer J., P.A. Vela. Personnel tracking on construction sites using video cameras. Advanced Engineering Informatics, 2009(23): 452~462
[47]周虎.河南国网宝泉抽水蓄能电站工程——堆石混凝土施工技术要求.清华大学水利系,黄河勘测规划设计有限公司,2006
[48]周虎.山西省晋城市围滩水电站工程——堆石混凝土施工技术要求.清华大学水利系,2009
[49] Berthold K.P. Horn, Brian G. Rhunck. Determining Optical Flow. Artificial Intelligence, 1981, 17(1-3): 185~203
[50] Bruce D. Lucas, Takeo Kanade. An iterative image registration technique with an application to stereo vision. Proceedings of the 1981 DARPA Image Understanding Workshop, 1981: 121~130
[51] Sasa G, Loncaric S. Spatio-temporal image segmentation using optical flow and clustering algorithm. First International Workshop on Image and Signal Processing and Analysis. Pula, Croatia, 2000: 63-68
[52]卜凡亮,王蓉,金华,李丽华.基于光流的运动目标检测与跟踪.中国人民公安大学学报(自然科学版),2009, 60(2): 58~60
[53]王亮.光流技术及其在运动目标检测和跟踪中的应用研究:[硕士学位论文].长沙:国防科技大学控制科学与工程系,2007
[54] Jain, R., Nagel, H. On the analysis of accumulative difference pictures from image sequences of real world scenes. IEEE Trans. Patt. Analy. Mach. Intell(1), 1979, 8(2): 206~214
[55]王栓,艾海舟,何克忠.基于差分图象的多运动目标的检测与跟踪.中国图象图形学报,1999,4(6): 470~475
[56]吕国亮,赵曙光,赵俊.基于三帧差分和连通性检验的图像运动目标检测新方法.液晶与显示,2007,22(1): 87~93
[57]邱道尹,张文静,顾波,刘新宇.帧差法在运动目标实时跟踪中的应用.华北水利水电学院学报,2009,30(3): 45~46
[58] Christof Ridder, Olaf Munkelt, Harald Kirchner. Adaptive Background Estimation and Foreground Detection using Kalman-Filtering. Proceedings of International Conference on recent Advances in Mechatronics,1995:193~199
[59] Wren C. R., A. Azarbayejani, T. Darrell, A. Pentland. Pfinder: Real-time tracking of the human body, IEEE Trans.on PAMI, 1997, 19(7): 780~785
[60] Stauffer C., W. Grimson. Adaptive background mixture models for real-time tracking. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999: 246~252
[61] Zivkovic Z. Improved Adaptive Gaussian Mixture Model for Background Subtraction. Proc. Int’l Conf. Pattern Recognition, 2004, 2: 28~31
[62] Rittscher J, J. Kato, S. Joga, A. Blake. A Probabilistic Background Model for Tracking. Proceedings of the 6th European Conference on Computer Vision-Part II, 2000: 336~350
[63] Elgammal A, Harwood D, Davis L. Non-parametric Model for Background Subtraction. Proceedings of the 6th European Conference on Computer Vision-Part II. 2000,1843: 751~767
[64]崔屹.图象处理与分析——数学形态学方法及应用.北京:科学出版社,2000
[65]喻杰,许化溪.一种易于实现的适于细胞图像连通区域的标记算法.江苏大学学报(医学版),2005, 15(02): 152~155
[66]陈柏生.一种二值图像连通区域标记的新方法.计算机工程与应用,2006(25): 46~47
[67] Rosenfeld A, J. Pfaltz. Sequential Operations in Digital Picture Processing. Journal of the ACM, 1966, 13(4): 471~494
[68] Luigidi Stefano, Andrea Bulgarelli. A Simple and Efficient Connected Components Labeling Algorithm. Proceedings of the 10th International Conference on Image Analysis and Processing, 1999: 322~328
[69] SUZUKI K, HORIBA I, SUGIE N. Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 2003, 89(1): 1~23
[70] HE L, CHAO Y Y, SUZUKI K. A Linear-Time Two-Scan Labeling Algorithm. IEEE International Conference on Image Processing, San Antonio, TX: IEEE, 2007: 241~244
[71] HE L, CHAO Y Y, SUZUKI K, WU K. Fast connected-component labeling. Pattern Recognition, 2009, 42(9): 1977~1987
[72] HE L, CHAO Y Y, SUZUKI K. An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recognition Letters, 2010, 31(1): 28~35
[73] Michael B. Dillencourt, Hannan Samet, Markku Tammenen. A general approach to connected component labeling for arbitrary image representations. Journal of the Association for Computing Machinery, 1992, 39(2): 253~280
[74] Vikrant Khannaa, Phalguni Guptaa, C.J. Hwang. Finding connected components in digital images by aggressive reuse of labels. Image and Vision Computing, 2002(20): 557~568
[75] WU K, OTOO E, SHOSHANI A. Optimizing Connected Component Labeling Algorithms. Proceedings of the SPIE Conference on Medical Imaging, San Diego, CA, USA: the International Society for Optical Engineering, 2005: 1965~1976
[76] WU K, OTOO E, SUZUKI K. Two Strategies to Speed up Connected Component Labeling Algorithms. Tech. Rep. 59102, Lawrence Berkeley National Lab (2005)
[77] WU K, OTOO E, SUZUKI K. Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications, 2009, 12(2): 117~135
[78] AIZAWA K, TANAKA S, MOTOMURA K, KADOWAKI R. Algorithms for Connected Component Labeling Based on Quadtrees. International Journal of Imaging Systems and Technology. 2009, 19(2): 158~166
[79]高红波,王卫星.一种二值图像连通区域标记的新算法.计算机应用,2007,27(11): 2776~2778
[80]蔡世界,于强.基于游程编码的连通区域标记算法优化及应用.计算机应用,2008,28(12): 3150~3153
[81] HE L, CHAO Y Y, SUZUKI K. A run-based two-scan labeling algorithm. IEEE Transactions on Image Processing, USA: IEEE, 2008: 749~756
[82]张二虎,冯江. Blob分析中基于游程链的连通区域标记.应用科学学报,2008,26(05): 536~540
[83] HE L, CHAO Y Y, SUZUKI K. A Run-Based One-Scan Labeling Algorithm. Proceedings of the 6th ICIAR, Canada: International Conference on Image Analysis and Recognition, 2009: 93~102
[84] CHANG F, CHEN C J, LU C J. A Linear-Time Component-Labeling Algorithm Using Contour Tracing. Technique. Computer Vision and Image Understanding, 2004, 93(2): 206~220
[85] Pierre F. Gabriel, Jacques G. Verly, Justus H. Piater, AndréGenon. The State of the Art in Multiple Object Tracking Under Occlusion in Video Sequences. In Advanced Concepts for Intelligent Vision Systems, 2003: 166~173
[86] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering, 1960, 82 (D): 35~45
[87] Erik Cuevas, Danie Zaldivar, Raul Rojas. Kalman filter for vision tracking. 2005
[88] K Fukunaga, L D Hostetler. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transaction on Information Theory (S0018-9448), 1975, 21(1): 32~40
[89] Robert T. Collins. Mean-shift Blob Tracking through Scale Space. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2003, 2: 234~240
[90] Dorin Comaniciu, Visvanathan Ramesh, Peter Meer. Real-Time tracking of non-rigid objects using mean shift. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000, 2 :142~149
[91] Bhattacharyya A.On a measure of divergence between two statistical populations defined by their probability distributions.Bull.Calcutta Math,1943,35:99~109
[92]李正周,刘国金.基于均值偏移和卡尔曼滤波的目标跟踪方法.弹箭与制导学报,2008,28(1): 71~74
[93]王展青,凡友福,张桂林.跟踪遮挡目标的一种鲁棒算法.计算机工程与应用,2007,43(27): 50~53
[94]王江涛,杨静宇.遮挡情况下基于Kalman均值偏移的目标跟踪.系统仿真学报,2007,19(18): 4216~4220
[95]吴成东,郭利锋,张云洲,刘濛.多车辆跟踪时目标粘连的解决方法.东北大学学报(自然科学版),2008,29(8): 1065~1068
[96]薛东军,平西建,臧传辉.基于测地形态学进行细胞图象分割的新方法.信息工程大学学报,2003,4(4): 88~92
[97]游迎荣,范影乐,庞全.基于距离变换的粘连细胞分割方法.计算机工程与应用,2005,20: 206~208
[98] Cecilia Di Ruberto, Andrew Dempster, Shahid Khan, Bill Jarra, Segmentation of blood images using morphological operators. Image and Vision Computing, 2002(20): 133~146
[99]印勇,李阿琼.一种粘连血细胞图像分割新方法.计算机工程与应用,2009,45(35): 173~175
[100] E.H. van den Berg, A.G.C.A. Meesters, J.A.M. Kenter, W. Schlager. Automated separation of touching grains in digital images of thin sections. Computers & Geosciences, 2002(28): 179~190
[101]马东,曹培杰,潘凯丽.分割重叠细胞核的方法及比较研究.北京大学生物医学工程,1999,18(3): 142~147
[102]薛东军,平西建,臧传辉.基于测地形态学进行细胞图象分割的新方法.信息工程大学学报,2003,4(4): 88~92.
[103] C. Lantuejoul, S. Beucher. On the use of geodesic metric in image analysis. J. Microsc. 1981(121): 39~49
[104] L. Vincent, P. Soille. Watersheds in digital spaces: an efficient algorithm based on immersion simulation. IEEE Trans. Pattern Anal. Mach. Intell. 1991(13): 583~598
[105]陆宗骐. C/C++图像处理编程.北京:清华大学出版社,2005.
[106] X. Song, F. Yamamoto, M. Iguchi, L. Shen, X. Ruan, K. Ishi, A method for measuring particle size in overlapped particle images. ISIJ Intl. 1998, 38: 971~976
[107] L. Shen, X. Song, M. Iguchi, F. Yamamoto, A method for recognizing particle in overlapped particle images. Pattern Recogn. Lett. 2000, 21: 21~30
[108]尤育赛,于慧敏.一种重叠红细胞图像的分离方法.中国图象图形学报,2005,10 (6): 736~740
[109]阴法明.一种粘连颗粒图像的快速分割方法.微处理机,2006,8(4): 80~81
[110]黄艳艳,曾致远.图像分析技术在粗粒土组构图像测量中的应用:[硕士学位论文].武汉:华中科技大学系统分析与集成系,2007
[111]黄碧霞,陆阳.基于数字图像处理的粗集料颗粒分布分析.路基工程,2008,1: 23~25
[112]高守传,姚领田等. Visual C++实践与提高——数字图像处理与工程应用篇.北京:中国铁道出版社,2006
[113]陈纯.计算机图像处理技术及算法.北京:清华大学出版社,2003
[114]陆其明. DirectShow开发指南.北京:清华大学出版社,2003
[115]李真文. SQL Server2000开发人员指南.北京:北京希望电子出版社,2001
[2] David A. Forsyth, Jean Ponce. Computer vision: a modern approach.林学訚,王宏等译.北京:电子工业出版社,2004
[3]冈萨雷斯.数字图像处理(第二版).阮秋琦等译.北京:电子工业出版社,2003
[4]章毓晋.图像分割.北京:科学出版社,2001
[5] Alper Yilmaz, Omar Javed, Mubarak Shah. Object Tracking: A Survey. ACM Comput. Surv., 2006, 38(4): 13.1~13.45
[6]黄声亨,曾怀恩. GPS实时监控系统碾压试验的高程精度评定.测绘信息与工程,2004,29(5): 39~40
[7]黄声享,刘经南,吴晓铭. GPS实时监控系统研制及其在堆石坝施工中的初步应用.武汉大学学报(信息科学版),2005,30(9): 13~16
[8]黄声亨,曾怀恩. GPS实时监控系统及其在碾压施工中的应用.测绘工程,2006,15(3): 23~25
[9]吴晓铭.水布垭大坝填筑碾压施工质量监控系统研究.湖北水力发电,2002,44(1): 27~28
[10]吴浩,王乾坤,陈沁,李岩.基于GPS/ GIS集成大坝碾压施工监控平台研究.武汉理工大学学报,2009,31(15): 45~48
[11]王乾坤,陈沁. GPS大坝施工碾压质量实时监控方法.武汉理工大学学报,2009,31(8): 79~82
[12]钟登华,毛寨汉,朱慧蓉.水利水电工程土石方开挖可视化设计方法初探.水利水电技术,2001,32 (12): 32~34
[13]钟登华,宋洋,黄河,郑家祥.基于GIS水电工程施工系统三维动态图形仿真技术.系统仿真学报,2003,15(12): 1766~1770
[14]钟登华,周锐,刘东海.水利水电工程施工系统三维建模与仿真.计算机仿真,2003,20(2): 86~91
[15]钟登华,南春辉,宋洋.水电工程施工进度三维动态可视化方法.天津大学学报,2005,38(4): 322~327
[16]黄少华,张德文,李小帅.虚拟现实技术在水利工程仿真中的应用.人民长江, 2006,37(5): 36~38
[17]黄少华,史那.基于VR与GIS的水利工程视算一体化系统的研制.测绘信息与工程,2007,32(5): 1~2
[18]任庆海,陈海霞.视频监控系统在水利工程中的应用.水利水文自动化,2004(1): 19~22
[19]关晓惠.视频监控技术研究及其在水利信息化中的应用.浙江水利水电专科学校学报,2006,18(1): 13~15
[20]蔡铂.浅谈视频监控系统在施工现场的运用.山西建筑,2008,34(14): 365~366
[21]张玉燕,黄晞淳,武佳枚.浅谈视频监控系统在水利行业中的应用趋势.水利科技,2009(8): 26~29
[22] http://www.it.com.cn/f/market/069/13/324276.htm
[23]秦少杰.赤峰市三座店水利枢纽工程网络及视频监控系统设计.吉林水利,2005(7): 41~42
[24]高春平,姜黎辉,何雄.远程实时视频数字监控系统在防汛抢险中的应用. 2004(4): 58~60
[25]王五平,宋人心,傅翔,罗骐先.用超声波CT探测混凝土内部缺陷.水利水运工程学报,2003(2): 56~60
[26]肖敏芳,李卓球.基于混凝土无损探伤的超声CT图像分析和处理:[硕士学位论文].武汉:武汉理工大学理学院,2006
[27]王宝庭,徐道远,宋玉普.数字图像处理技术在混凝土研究中的应用.河海大学学报,1999,27(4): 60~63
[28]李楠,王晓琴,杨吉新.混凝土外观质量智能评判技术.交通科技,2006,218(5): 97~99
[29]耿飞,钱春香.图像分析技术在混凝土收缩裂缝定量测试与评价中的应用研究.东南大学学报(自然科学版),2003(6): 773~776
[30]耿飞,解建光,钱春香.图像分析技术对混凝土裂缝的定量评价.混凝土,2005,187(5): 78~80
[31]刘清元,谈桥.基于图像处理的混凝土裂缝的检测.武汉理工大学学报,2005,27(4): 69~71
[32]王伟,何小元,杨福俊,尹兰.基于数字光测的表面裂缝宽度图像分析法.测控技术,2006,25(7): 23~25
[33]王雷,黄战华,蔡怀宇,刘正.工程结构构件裂缝图像的处理与宽度测量.计算机测量与控制,2006,14(8): 992~993
[34] Benning Wilhelm, Lange Johannes, Schwermann Raimund, Effkemann Christoph, G?rtz S. Monitoring crack origin and evolution at concrete elements using photogrammetry. In , ISPRS(International Society of Photogrammetry and Remote Sensing), Commission V, 2004:678~683
[35] Lange Johannes, Benning Wilhelm, Siering Karen. Crack detection at concrete construction units from photogrammetric data using image processing procedures. In , ISPRS Mid-term Symposium 2006, Commission VI WG VI/4, 2006
[36]于承新,徐芳,黄桂兰,刘友光.近景摄影测量在钢结构变形监测中的应用.山东建筑工程学院学报,15(4),2000: 1~7
[37]曲哲,陆新征,叶列平,程锦.数字摄影测量在混凝土梁受剪性能研究中的应用.建筑结构学报,27(2),2006: 936~939
[38] Psaltis C., C.Ioannidis. An automatic technique for accurate non-contact structural deformation measurements. IAPRS Volume XXXVI, Part5, Dresden, 2006
[39]张国栋,赵文光,文银平.结构动态位移监测图像处理技术的工程应用.华中科技大学学报,2005(22): 10~12
[40] Tournas L, Tsakiri M, Kattis M. Displacement monitoring at the micron level using digital photogrammetry. 3rd IAG/12th FIG Symposium, Baden, May22-24, 2006
[41]李文炳,冯平,蔡增伸,叶蔚嫦.基于数字图像处理的位移测量.浙江工业大学学报,2004,32(6): 688~691
[42] Ioannis K. Brilakis, Lucio Soibelman, Yoshihisa Shinagawa. Construction site image retrieval based on material cluster recognition. Advanced Engineering Informatics, 2006, 20:443~452
[43] Xiaonan Zhang, Nick Bakis, Timothy C. Lukins, et al. Automating progress measurement of construction projects. Automation in Construction. 2009, 18:294~301
[44] Y.M. Ibrahim, T.C. Lukins, X. Zhang. Towards automated progress assessment of workpackage components in construction projects using computer vision. Advanced Engineering Informatics, 2009, 23:93~103
[45] R.Navon. Automated project performance control of construction projects. Automation in Construction, 2005, 14 (4):467~476.
[46] Teizer J., P.A. Vela. Personnel tracking on construction sites using video cameras. Advanced Engineering Informatics, 2009(23): 452~462
[47]周虎.河南国网宝泉抽水蓄能电站工程——堆石混凝土施工技术要求.清华大学水利系,黄河勘测规划设计有限公司,2006
[48]周虎.山西省晋城市围滩水电站工程——堆石混凝土施工技术要求.清华大学水利系,2009
[49] Berthold K.P. Horn, Brian G. Rhunck. Determining Optical Flow. Artificial Intelligence, 1981, 17(1-3): 185~203
[50] Bruce D. Lucas, Takeo Kanade. An iterative image registration technique with an application to stereo vision. Proceedings of the 1981 DARPA Image Understanding Workshop, 1981: 121~130
[51] Sasa G, Loncaric S. Spatio-temporal image segmentation using optical flow and clustering algorithm. First International Workshop on Image and Signal Processing and Analysis. Pula, Croatia, 2000: 63-68
[52]卜凡亮,王蓉,金华,李丽华.基于光流的运动目标检测与跟踪.中国人民公安大学学报(自然科学版),2009, 60(2): 58~60
[53]王亮.光流技术及其在运动目标检测和跟踪中的应用研究:[硕士学位论文].长沙:国防科技大学控制科学与工程系,2007
[54] Jain, R., Nagel, H. On the analysis of accumulative difference pictures from image sequences of real world scenes. IEEE Trans. Patt. Analy. Mach. Intell(1), 1979, 8(2): 206~214
[55]王栓,艾海舟,何克忠.基于差分图象的多运动目标的检测与跟踪.中国图象图形学报,1999,4(6): 470~475
[56]吕国亮,赵曙光,赵俊.基于三帧差分和连通性检验的图像运动目标检测新方法.液晶与显示,2007,22(1): 87~93
[57]邱道尹,张文静,顾波,刘新宇.帧差法在运动目标实时跟踪中的应用.华北水利水电学院学报,2009,30(3): 45~46
[58] Christof Ridder, Olaf Munkelt, Harald Kirchner. Adaptive Background Estimation and Foreground Detection using Kalman-Filtering. Proceedings of International Conference on recent Advances in Mechatronics,1995:193~199
[59] Wren C. R., A. Azarbayejani, T. Darrell, A. Pentland. Pfinder: Real-time tracking of the human body, IEEE Trans.on PAMI, 1997, 19(7): 780~785
[60] Stauffer C., W. Grimson. Adaptive background mixture models for real-time tracking. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999: 246~252
[61] Zivkovic Z. Improved Adaptive Gaussian Mixture Model for Background Subtraction. Proc. Int’l Conf. Pattern Recognition, 2004, 2: 28~31
[62] Rittscher J, J. Kato, S. Joga, A. Blake. A Probabilistic Background Model for Tracking. Proceedings of the 6th European Conference on Computer Vision-Part II, 2000: 336~350
[63] Elgammal A, Harwood D, Davis L. Non-parametric Model for Background Subtraction. Proceedings of the 6th European Conference on Computer Vision-Part II. 2000,1843: 751~767
[64]崔屹.图象处理与分析——数学形态学方法及应用.北京:科学出版社,2000
[65]喻杰,许化溪.一种易于实现的适于细胞图像连通区域的标记算法.江苏大学学报(医学版),2005, 15(02): 152~155
[66]陈柏生.一种二值图像连通区域标记的新方法.计算机工程与应用,2006(25): 46~47
[67] Rosenfeld A, J. Pfaltz. Sequential Operations in Digital Picture Processing. Journal of the ACM, 1966, 13(4): 471~494
[68] Luigidi Stefano, Andrea Bulgarelli. A Simple and Efficient Connected Components Labeling Algorithm. Proceedings of the 10th International Conference on Image Analysis and Processing, 1999: 322~328
[69] SUZUKI K, HORIBA I, SUGIE N. Linear-time connected-component labeling based on sequential local operations. Computer Vision and Image Understanding, 2003, 89(1): 1~23
[70] HE L, CHAO Y Y, SUZUKI K. A Linear-Time Two-Scan Labeling Algorithm. IEEE International Conference on Image Processing, San Antonio, TX: IEEE, 2007: 241~244
[71] HE L, CHAO Y Y, SUZUKI K, WU K. Fast connected-component labeling. Pattern Recognition, 2009, 42(9): 1977~1987
[72] HE L, CHAO Y Y, SUZUKI K. An efficient first-scan method for label-equivalence-based labeling algorithms. Pattern Recognition Letters, 2010, 31(1): 28~35
[73] Michael B. Dillencourt, Hannan Samet, Markku Tammenen. A general approach to connected component labeling for arbitrary image representations. Journal of the Association for Computing Machinery, 1992, 39(2): 253~280
[74] Vikrant Khannaa, Phalguni Guptaa, C.J. Hwang. Finding connected components in digital images by aggressive reuse of labels. Image and Vision Computing, 2002(20): 557~568
[75] WU K, OTOO E, SHOSHANI A. Optimizing Connected Component Labeling Algorithms. Proceedings of the SPIE Conference on Medical Imaging, San Diego, CA, USA: the International Society for Optical Engineering, 2005: 1965~1976
[76] WU K, OTOO E, SUZUKI K. Two Strategies to Speed up Connected Component Labeling Algorithms. Tech. Rep. 59102, Lawrence Berkeley National Lab (2005)
[77] WU K, OTOO E, SUZUKI K. Optimizing two-pass connected-component labeling algorithms. Pattern Analysis and Applications, 2009, 12(2): 117~135
[78] AIZAWA K, TANAKA S, MOTOMURA K, KADOWAKI R. Algorithms for Connected Component Labeling Based on Quadtrees. International Journal of Imaging Systems and Technology. 2009, 19(2): 158~166
[79]高红波,王卫星.一种二值图像连通区域标记的新算法.计算机应用,2007,27(11): 2776~2778
[80]蔡世界,于强.基于游程编码的连通区域标记算法优化及应用.计算机应用,2008,28(12): 3150~3153
[81] HE L, CHAO Y Y, SUZUKI K. A run-based two-scan labeling algorithm. IEEE Transactions on Image Processing, USA: IEEE, 2008: 749~756
[82]张二虎,冯江. Blob分析中基于游程链的连通区域标记.应用科学学报,2008,26(05): 536~540
[83] HE L, CHAO Y Y, SUZUKI K. A Run-Based One-Scan Labeling Algorithm. Proceedings of the 6th ICIAR, Canada: International Conference on Image Analysis and Recognition, 2009: 93~102
[84] CHANG F, CHEN C J, LU C J. A Linear-Time Component-Labeling Algorithm Using Contour Tracing. Technique. Computer Vision and Image Understanding, 2004, 93(2): 206~220
[85] Pierre F. Gabriel, Jacques G. Verly, Justus H. Piater, AndréGenon. The State of the Art in Multiple Object Tracking Under Occlusion in Video Sequences. In Advanced Concepts for Intelligent Vision Systems, 2003: 166~173
[86] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering, 1960, 82 (D): 35~45
[87] Erik Cuevas, Danie Zaldivar, Raul Rojas. Kalman filter for vision tracking. 2005
[88] K Fukunaga, L D Hostetler. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transaction on Information Theory (S0018-9448), 1975, 21(1): 32~40
[89] Robert T. Collins. Mean-shift Blob Tracking through Scale Space. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2003, 2: 234~240
[90] Dorin Comaniciu, Visvanathan Ramesh, Peter Meer. Real-Time tracking of non-rigid objects using mean shift. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2000, 2 :142~149
[91] Bhattacharyya A.On a measure of divergence between two statistical populations defined by their probability distributions.Bull.Calcutta Math,1943,35:99~109
[92]李正周,刘国金.基于均值偏移和卡尔曼滤波的目标跟踪方法.弹箭与制导学报,2008,28(1): 71~74
[93]王展青,凡友福,张桂林.跟踪遮挡目标的一种鲁棒算法.计算机工程与应用,2007,43(27): 50~53
[94]王江涛,杨静宇.遮挡情况下基于Kalman均值偏移的目标跟踪.系统仿真学报,2007,19(18): 4216~4220
[95]吴成东,郭利锋,张云洲,刘濛.多车辆跟踪时目标粘连的解决方法.东北大学学报(自然科学版),2008,29(8): 1065~1068
[96]薛东军,平西建,臧传辉.基于测地形态学进行细胞图象分割的新方法.信息工程大学学报,2003,4(4): 88~92
[97]游迎荣,范影乐,庞全.基于距离变换的粘连细胞分割方法.计算机工程与应用,2005,20: 206~208
[98] Cecilia Di Ruberto, Andrew Dempster, Shahid Khan, Bill Jarra, Segmentation of blood images using morphological operators. Image and Vision Computing, 2002(20): 133~146
[99]印勇,李阿琼.一种粘连血细胞图像分割新方法.计算机工程与应用,2009,45(35): 173~175
[100] E.H. van den Berg, A.G.C.A. Meesters, J.A.M. Kenter, W. Schlager. Automated separation of touching grains in digital images of thin sections. Computers & Geosciences, 2002(28): 179~190
[101]马东,曹培杰,潘凯丽.分割重叠细胞核的方法及比较研究.北京大学生物医学工程,1999,18(3): 142~147
[102]薛东军,平西建,臧传辉.基于测地形态学进行细胞图象分割的新方法.信息工程大学学报,2003,4(4): 88~92.
[103] C. Lantuejoul, S. Beucher. On the use of geodesic metric in image analysis. J. Microsc. 1981(121): 39~49
[104] L. Vincent, P. Soille. Watersheds in digital spaces: an efficient algorithm based on immersion simulation. IEEE Trans. Pattern Anal. Mach. Intell. 1991(13): 583~598
[105]陆宗骐. C/C++图像处理编程.北京:清华大学出版社,2005.
[106] X. Song, F. Yamamoto, M. Iguchi, L. Shen, X. Ruan, K. Ishi, A method for measuring particle size in overlapped particle images. ISIJ Intl. 1998, 38: 971~976
[107] L. Shen, X. Song, M. Iguchi, F. Yamamoto, A method for recognizing particle in overlapped particle images. Pattern Recogn. Lett. 2000, 21: 21~30
[108]尤育赛,于慧敏.一种重叠红细胞图像的分离方法.中国图象图形学报,2005,10 (6): 736~740
[109]阴法明.一种粘连颗粒图像的快速分割方法.微处理机,2006,8(4): 80~81
[110]黄艳艳,曾致远.图像分析技术在粗粒土组构图像测量中的应用:[硕士学位论文].武汉:华中科技大学系统分析与集成系,2007
[111]黄碧霞,陆阳.基于数字图像处理的粗集料颗粒分布分析.路基工程,2008,1: 23~25
[112]高守传,姚领田等. Visual C++实践与提高——数字图像处理与工程应用篇.北京:中国铁道出版社,2006
[113]陈纯.计算机图像处理技术及算法.北京:清华大学出版社,2003
[114]陆其明. DirectShow开发指南.北京:清华大学出版社,2003
[115]李真文. SQL Server2000开发人员指南.北京:北京希望电子出版社,2001
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