田间叶片图像分割与单幅三维重建的机器视觉算法研究
|
摘要
随着计算机技术和物联网的快速发展,运用机器视觉技术实现农业生产管理智能决策已成为可能。植株农学性状参数可视化获取是农业生产全过程数字化管理的重要一环。研究田间叶片图像分割与图像三维重建算法,实现农学性状参数的实时远程提取,对解决生产管理全过程的智能决策支持的问题具有重要的理论和现实意义。
本文以果园田间叶片为研究对象,利用机器视觉系统解决远程获取农业生产现场叶片的农学性状参数的问题。综合机器视觉技术、图像处理技术和模式识别技术,研究自适应阈值的在线叶片图像分割方法。利用分割出的叶片区域研究由单幅图像恢复叶片三维表面的方法,计算叶面积。利用实时远程提取得到的农学性状参数,为生产管理提供智能决策支持。重点展开了以下几个方面的工作：
①针对自然光条件下,由实时在线系统拍摄的具有复杂背景的田间单片植物叶片分割成功率不高的问题,提出了一种自适应阈值的灰度映射函数,该映射方法使得运用本文的分割算法可以获得较高的分割成功率。在结合Canny和OTSU算子优势的分割算法,有效剔除了复杂的背景和强干扰日标的基础上,对基于边缘检测和基于闽值的分割方法不能去除的叶片边缘背景噪声,设计了丛于数学形态学和内外模板的优化方法,该方法优化分割结果后获取的目标区域连续,去除了与背景的粘连,使得叶片分割准确率提高。
②针对不同形态特征的植物叶片分割后的叶片日标区域的提取问题,设计了叶形判别算法和三种不同的连通区域提取方法,可更加准确地将叶片从分割后的连通区域中识别出来,该方法使得分割获得完整叶片目标率提高。同时对二维图像中无法对现场叶片位姿判断的问题,设计了叶片的位姿模板库,通过提取不同三维角度的叶片位姿的归一化形状在二维的投影特征,运用K-近邻空间聚类匹配模板,获取叶片位姿信息,并提出了两个仅与叶片边缘点相关的矩(弦长矩和弦夹角矩)。实验结果表明,叶片分割成功率达到72.5%。
③建立根据表面明暗变化的单幅图像三维重建算法。在求得朗伯体法矢量的p,q值和灰度值拟合方程的基础上,建立了由微朗勃体组成的基于邻域迭代的叶片的连续可积曲面方程,并利用约束方程,获得该曲面解系的一组解,利用该曲面上每一像素点与微朗伯体的切点构建三角网计算叶面积。针对叶片的亮度值跳跃区域重建效果的调整需要,研究了一个有效的三角形插值方法,从而实现了由单幅图像重建叶片三维表面的目标。
④集成了基于WEB和Internet的远程网络控制机器视觉设备系统,该系统能够根据控制算法和反馈信号来规定摄像设备的拍摄运动规则,并能远程实时调度设备运动,同时可实时获取现场设备的参数和图像数据。本文设计的机器视觉系统可以为图像分割与三维重建方法提供样本图像,并将图像识别结果反馈给决策系统,得到的植物性状参数可以为后续工作中的温室生产管理智能决策系统提供支持。
With the rapid development of computer technology and the Internet of things. to realize orchard production intelligent decision management by using the machine vision technology has become possible. To obtain the plant agronomic parameters visually is a important part of the whole process digital management of orchard production.The study on orchard leaf image segmentation and image reconstruction algorithm. is to solve the intelligent decision problems of production management.by the way of the real-time remote extraction of agronomic parameters in. This has important theoretical and practical significance.
This paper. using orchard field leaves as the object of study. solve the problem of extracting parameters of agronomic using machine vision system by the remote access to agricultural production spot. Combining Integrated machine vision technology, image processing technology and pattern recognition technology, we research the segmentation method of online leaf image using adaptive threshold. Stduy the3D reconstruction method for recovering three-dimensional surface from a single image based on the region segmentation of leaf, and calculate leaf area. The usage of the real-time remote extracted agronomic parameters, can provide intelligent decision support for the production management. Focus on the following aspects of the work:
QJln natural light conditions, some segmentation success ratio of field plant leaf with complex background captured by a real-time system is not high. In this paper we propose the gray mapping function and an adaptive threshold segmentation, the mapping method makes the segmentation algorithm used in this paper can obtain higher segmentation success rate. In the segmentation algorithm,we combine the edges segemented Canny and OTSU operator, remove the complex background and the strong interference targets.For the blade edge background noise, that cannot be removed by the operators based on edge detection and threshold segmentation, we design optimization method based on mathematical morphology and internal and external template, the target area segmentation results obtained after the optimization method continuous, remove adhesions and background, the leaf segmentation effect is very ideal.
(2)In order to solve the problem of extracting the target area of plant leaf blades of different morphological features after segmentation, shape recognition algorithm and three kinds of connected region extraction method is designed, leaf area can be successfully identified from the connected region after segmentation, the method makes the segmentation obtaining complete leaf ratio is greatly improved. At the same time, the two-dimensional image is impossible in the problem of field vane position judgment. We design a template library of blade pose, extract the leaf different3D pose information from the projection of normalized shape characteristics of two-dimensional. We use K-nearest neighbor clustering to match the template, obtaining leaf pose information, and from the two only leaves the edge points related moment (Xian Changju chord angle moment).
③Set up the algorithm of3D reconstruction from single image surface according to brightness change. In Lambertian modle. using the fitting equation of the p.q value of the surface vector and the gray value, we establish the continuous integrable equation by blade micro Lambertian body composition. and use the constraint equation. obtain a set of solutions of the surface system of solutions. We use the point of each pixel on the surface of micro Lambertian body to construction triangulation to calculate leaf area.To meet the effect adjustment needs of brightness jump of the blade region reconstruction. we research an effective triangular interpolation method.
④We integrate the remote network control machine vision system based on WEB and Internet.The system can set camera shooting movement rules. and remote real-time schedule the equipment movement, according to the control algorithm and the feedback signal. The system also can obtain the equipment parameters and real-time image data. Machine vision system designed in this paper can provide sample images for image segmentation method and leaf area calculation. and image recognition results feedback to the decision system, parameters of the plant traits can give support for the follow-up work of the greenhouse production management intelligent decision system.
本文以果园田间叶片为研究对象,利用机器视觉系统解决远程获取农业生产现场叶片的农学性状参数的问题。综合机器视觉技术、图像处理技术和模式识别技术,研究自适应阈值的在线叶片图像分割方法。利用分割出的叶片区域研究由单幅图像恢复叶片三维表面的方法,计算叶面积。利用实时远程提取得到的农学性状参数,为生产管理提供智能决策支持。重点展开了以下几个方面的工作：
①针对自然光条件下,由实时在线系统拍摄的具有复杂背景的田间单片植物叶片分割成功率不高的问题,提出了一种自适应阈值的灰度映射函数,该映射方法使得运用本文的分割算法可以获得较高的分割成功率。在结合Canny和OTSU算子优势的分割算法,有效剔除了复杂的背景和强干扰日标的基础上,对基于边缘检测和基于闽值的分割方法不能去除的叶片边缘背景噪声,设计了丛于数学形态学和内外模板的优化方法,该方法优化分割结果后获取的目标区域连续,去除了与背景的粘连,使得叶片分割准确率提高。
②针对不同形态特征的植物叶片分割后的叶片日标区域的提取问题,设计了叶形判别算法和三种不同的连通区域提取方法,可更加准确地将叶片从分割后的连通区域中识别出来,该方法使得分割获得完整叶片目标率提高。同时对二维图像中无法对现场叶片位姿判断的问题,设计了叶片的位姿模板库,通过提取不同三维角度的叶片位姿的归一化形状在二维的投影特征,运用K-近邻空间聚类匹配模板,获取叶片位姿信息,并提出了两个仅与叶片边缘点相关的矩(弦长矩和弦夹角矩)。实验结果表明,叶片分割成功率达到72.5%。
③建立根据表面明暗变化的单幅图像三维重建算法。在求得朗伯体法矢量的p,q值和灰度值拟合方程的基础上,建立了由微朗勃体组成的基于邻域迭代的叶片的连续可积曲面方程,并利用约束方程,获得该曲面解系的一组解,利用该曲面上每一像素点与微朗伯体的切点构建三角网计算叶面积。针对叶片的亮度值跳跃区域重建效果的调整需要,研究了一个有效的三角形插值方法,从而实现了由单幅图像重建叶片三维表面的目标。
④集成了基于WEB和Internet的远程网络控制机器视觉设备系统,该系统能够根据控制算法和反馈信号来规定摄像设备的拍摄运动规则,并能远程实时调度设备运动,同时可实时获取现场设备的参数和图像数据。本文设计的机器视觉系统可以为图像分割与三维重建方法提供样本图像,并将图像识别结果反馈给决策系统,得到的植物性状参数可以为后续工作中的温室生产管理智能决策系统提供支持。
With the rapid development of computer technology and the Internet of things. to realize orchard production intelligent decision management by using the machine vision technology has become possible. To obtain the plant agronomic parameters visually is a important part of the whole process digital management of orchard production.The study on orchard leaf image segmentation and image reconstruction algorithm. is to solve the intelligent decision problems of production management.by the way of the real-time remote extraction of agronomic parameters in. This has important theoretical and practical significance.
This paper. using orchard field leaves as the object of study. solve the problem of extracting parameters of agronomic using machine vision system by the remote access to agricultural production spot. Combining Integrated machine vision technology, image processing technology and pattern recognition technology, we research the segmentation method of online leaf image using adaptive threshold. Stduy the3D reconstruction method for recovering three-dimensional surface from a single image based on the region segmentation of leaf, and calculate leaf area. The usage of the real-time remote extracted agronomic parameters, can provide intelligent decision support for the production management. Focus on the following aspects of the work:
QJln natural light conditions, some segmentation success ratio of field plant leaf with complex background captured by a real-time system is not high. In this paper we propose the gray mapping function and an adaptive threshold segmentation, the mapping method makes the segmentation algorithm used in this paper can obtain higher segmentation success rate. In the segmentation algorithm,we combine the edges segemented Canny and OTSU operator, remove the complex background and the strong interference targets.For the blade edge background noise, that cannot be removed by the operators based on edge detection and threshold segmentation, we design optimization method based on mathematical morphology and internal and external template, the target area segmentation results obtained after the optimization method continuous, remove adhesions and background, the leaf segmentation effect is very ideal.
(2)In order to solve the problem of extracting the target area of plant leaf blades of different morphological features after segmentation, shape recognition algorithm and three kinds of connected region extraction method is designed, leaf area can be successfully identified from the connected region after segmentation, the method makes the segmentation obtaining complete leaf ratio is greatly improved. At the same time, the two-dimensional image is impossible in the problem of field vane position judgment. We design a template library of blade pose, extract the leaf different3D pose information from the projection of normalized shape characteristics of two-dimensional. We use K-nearest neighbor clustering to match the template, obtaining leaf pose information, and from the two only leaves the edge points related moment (Xian Changju chord angle moment).
③Set up the algorithm of3D reconstruction from single image surface according to brightness change. In Lambertian modle. using the fitting equation of the p.q value of the surface vector and the gray value, we establish the continuous integrable equation by blade micro Lambertian body composition. and use the constraint equation. obtain a set of solutions of the surface system of solutions. We use the point of each pixel on the surface of micro Lambertian body to construction triangulation to calculate leaf area.To meet the effect adjustment needs of brightness jump of the blade region reconstruction. we research an effective triangular interpolation method.
④We integrate the remote network control machine vision system based on WEB and Internet.The system can set camera shooting movement rules. and remote real-time schedule the equipment movement, according to the control algorithm and the feedback signal. The system also can obtain the equipment parameters and real-time image data. Machine vision system designed in this paper can provide sample images for image segmentation method and leaf area calculation. and image recognition results feedback to the decision system, parameters of the plant traits can give support for the follow-up work of the greenhouse production management intelligent decision system.
引文
[1]Yunyoung Nam, Eenjun Hwang, Dongyoon Kim, A similarity-based leaf image retrieval scheme:Joining shape and venation features, Computer Vision and Image Understanding,2008, 110(2):245-259.
[2]TinoDornbusch, Bruno Andrieu, Lamina2Shape-An image processing tool for an exp] icit description of lamina shape tested on winter wheat, Computers and Electronics in Agriculture, 2010,70(1):217-224.
[3]Liu Xiaochuan, AiJiaoyan, TangJiliang, et al. The Segmentation of Black Rot Lesion of Cruciferous Plant Based on Self-adaptive Fuzzy Threshold, Journal of Image and Graphics, 2009,14(7):1334-1340.
[4]Joao Camargo Neto, George E. Meyer, David D. Jones, Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm, Computers and Electronics in Agriculture,2006,51(1-2):66-85.
[5]Mao Hanping, ZhangYancheng, Hu Bo, Segmentation of crop disease leaf images using fuzzy C-means clustering algorithm, Transactions of the Chinese Society of Agricultural Engineering,2008,24(9):136-140.
[6]Sun Jun, Improved 2D maximum between-cluster variance algorithm and its application to cucumber target segmentation, Transactions of the CSAE,2009,25(10):176-181.
[7]Qian Dan, Research on Influencing Factors of Image Segmentation for Crop and Weed Identification[D], Jiangsu:Jiangsu University,2006.
[8]KristianKirk, Hans Jorgen Andersen, AntonG. Thomsen, et al. Estimation of leaf area index in cereal crops using red-green images, Biosystems Engineering,2009,104(3):308-317.
[9]Li Zhengming, WangSen, Sun Jun, Image Segmentation in Object Recognition of Mature Eggplant, Transactions of the Chinese Society of Agricultural Machinery,2009,40:105-109.
[10]Zhang Yajing, LiMinzan, LiuGang, et al. Separating Adjoined Apples Based on Machine Vision and Information Fusion, Transactions of the Chinese Society of Agricultural Machinery,2009,40(11):180-183.
[11]Xiaofeng Wang, Deshuang Huang, JixiangDu, et al. Classification of plant leaf images with complicated background, Applied Mathematics and Computation,2008, 205(2):916-926.
[12]Zhao Jinhui, LuoXiwen, ZhouZhiyan, Image Segmentation Method for Sugarcane. Diseases Based on Color and Shape Features, Transactions of the Chinese Society of Agricultural Machinery,2008,39(9):100-104.
[13]Dong Jinyong, Wang Jianlun, Li Daoliang, et al. Research on Complex Target Image of Field Jujube Leaf Segmentation Based on Integrated Technology, Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, v 42, n 1, pp.165-170, January 2011
[14]D. He, Y. Bai, Y. Wang, H. Wu, A Crop Field Remote Monitoring System Based on Web-Server-Embedded Technology and CDMA Service, in Proc. SAINT Workshops,2007.
[15]Qinghang He, Zhenxi Zhang, A new edge detection algorithm for image corrupted by White-Gaussian noise, International Journal of Electronics and Communications,2007,61(8): 546-550
[16]Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, et al. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods, Computerized Medical Imaging and Graphics,2008,32(8):720-727
[17]K. Chen, Ch. Qin. Segmentation of beef marbling based on vision threshold, Computers and Electronics in Agriculture,2008,62(2):223-230
[18]S. Arora, J. Acharya, A. Verma, Prasanta K. Panigrahi, Multilevel thresholding for image segmentation through a fast statistical recursive algorithm, Pattern Recognition Letters,2008,29(2):119-125
[19]Marco A. G. de Carvalho, Roberto de A. Lotufo, Michel Couprie, Morphological segmentation of yeast by image analysis, Image and Vision Computing,2007,25(6):34-39
[20]Kamal Hammouche, Moussa Diaf, Patrick Siarry. A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation, Computer Vision and Image Understanding,2008,109(2):163-175.
[21]S. Arora, J. Acharya, A. Verma, Prasanta K. Panigrahi, Multilevel thresholding for image segmentation through a fast statistical recursive algorithm, Pattern Recognition Letters,2008,29(2):119-125.
[22]Jifeng Ninga, Lei Zhang, David Zhang, Chengke Wu, Interactive image segmentation by maximal similarity based region merging, Pattern Recognition,2009.03.004.
[23]Maria Frucci, Giuliana Ramella, Gabriella Sanniti di Bajam Using resolution pyramids for watershed image segmentation, Image and Vision Computing,2007, 25(6):1021-1031.
[24]Ardeshir Goshtasby, Piecewise cubic mapping functions for image registration, Pattern Recognition, Volume 20, Issue 5,1987, pp.525-533.
[25]S. Anna Durai, and E. Anna Saro, Image Mapping with Cumulative Distribution Function for Quick Convergence of Counter Propagation Neural Networks in Image Compression, World Academy of Science, Engineering and Technology 16,2006, pp.43-48.
[26]Lei Huang; Linrang Zhang; Shunjun Wu; A new method for noise subspace estimation based on the spatial smoothing Lanczos algorithm, Antennas and Propagation Society International Symposium,2004. IEEE,20-25 June 2004 pp.3689-3692 Vol.4
[27]P. E. Wellstead, G. R. Wagner, J. R. Caldas-Pinto, Two-dimensional adaptive prediction, smoothing and filtering, IEE PROCEEDINGS, Vol.134, Pt. F, No.3, JUNE 1987
[28]Ming-Kuei Hu. Visual pattern recognition by moment invariants [J]. Information Theory, IRE Transactions on,1962,8(2):179-187.
[29]Dingmingyue, Changjinling, Pengjiaxiong. Research on moment invariant, algorithm [J].Journal of data acquisition and processing,1992,7(1):1-9.
[30]Huangyong, Wangchongjun, Wangliang, Hangyan, Chenchaoqian. Research on algorithm of image retrieval based on shape moment invariants [J]. Computer Application Research, 2004,21(7):256-257.
[31]于峰等.一种基于图像特征值算法的叶面积测定方法.中国农业大学学报,2007(4)：67-69
[32]Yud-Ren Chen, Kuanglin Chao, Moon S. Kim. Machine vision technology for agricultural applications [J]. Computers and Electronics in Agriculture,2002,36(2) 173-191.
[33]B. K. P. Horn. Shape from Shading:A method for Obtaining the Shape of a Smooth Opaque object from One View[J]. Ph. D.. dissertation:Massachusettes Institute of Technology.1970,9(1):129-134.
[34]A Bruckstein. on Shape from Shading[J]. CVGIP,1987 44:139-154.
[35]S. Trandri, YH. Yang. Comparison of two shape-from-shading algorithms[J]. Pattern Recognition Letter,1990,11:637-642
[36]E. Rouy, A. Tourin. A Viscosity Solutions Approach to Shape from Shading[J]. SIAM J. Numberical Alnalysis,1992,29(3):867-884.
[37]J.Oliensis. Shape from Shading as s Partially Well-Constrained Problem[J].CVGIP,1991,S4:163-183.
[38]P. Dupuis, J. Oliensis. Direct Method for Reconstructing Shape from Shading[J]. IEEE Proc. Computer Vision and Pattern Recognition,1992,453-458.
[39]J. Oliensis, P. Dupuis. A Global Algorithm for Shape from Shad ing [J]. In:Proc. Int'1 Conf. Computer Vision,1993,692-701.
[40]M. Bichsel, A. P. Pentland. A Simple Algorithm for Shape from Shading[J]. IEEE Proc. Computer Vision and Pattern Recognition[J],1992,459-465.
[41]R. Kimmel, A. M. Bruckstein. Shape from Shading via Level Sets[J]. Israel Inst. Of Technology,1992, CIS Report #9209.
[42]K. lkeuchi, B.Horn. Numerical Shape from Shading and Occluding Boundaries[J]. Artificial Intelligence,1981,17:141-184.
[43]B. K. P. Horn, M. J. Brooks. The Variational Approach to Shape from Shading[J].CVGIP,1986,33:174-208.
[44]R. T. Frankot, R. Chellapa. A Method for Enforcing Integrability in Shape from Shading Algorithms[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1988,10:439-451.
[45]M. J. Brooks, B. K. P. Horn. Shape and Shading Algorithms [J]. In:Proc. Int'1 Joint Conf. on Artificial Intelligence,1985,932-936.
[46]S. Bakshi. A Study of Shape from Shading Algorithms[J]. Proc. the Fifth Annual Graduate Symposium Computational Science,1993,214-225.
[47]B. K. P. Horn. Height and Gradient from Shading[J]. Int's J. Computer Vision,1989,37-75.
[48]R. Szeliski. Fast Shape from Shading[J].CVGIP,1991,53:129-153.
[49]0. E. Vega, YH. Yang. Shaping Logic:A Heuristic Approch to Recover Shape from Shading[J]. IEEE Trans:Parttern Analysis and Machine Intelligence,1993,15(6):592-597.
[50]Q. Zheng, R. Chellppa. Estimating of Illumimamt Direction, Albedo, and Shape from Shading[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1991,13(7):680-702.
[51]Y. G. Lecelerc, A. F. Boick. The Direct Computation of Height from Shading[J]. IEEE Proc. Computer Vision and Pattern Recognition,1991,552-558.
[52]K. M. Lee, C. C. J. Kuo. Shape from Shading with a Linear Trangular Elemrnt Surface Model[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1993,15(8):815-882.
[53]A. P. Pentland. Local Shading Analysis[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1993,15(8):815-882.
[54]C. H. Lee and A. Rosenfeld. Improved Methods of Estimating Shape from Shading Using the Light Source Coordinate System[J]. Artificial Intelligence,1985,26:125-143.
[55]F. Ferrie, M. Levine. Where and Why Local Shading Analysis Work[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1989,11:198-206.
[56]A. P. Pentland. Liener Shape from Shading[J]. Computer Vision,1990,4(2):153-162.
[57]K. Ikenchi, B.Horn. Numerical Shape from Shading and Occluding Boundaries[J]. Artificial Intelligence,1981,17(8):141-184.
[58]B.T.Phong. Illumination for Computer Generated Images[J]. Ph. D. Dissertation:University of Utah,1973(7):102-106.
[59]'J.F. Blinn. Model of Light Reflection for Computer Synthesiszed Pictures[J]. Computer Graphics,1977,11(2):192-198.
[60]Werner A. Skalski K. Reverse Engineering of Free from Surfaces. Materials Processing Technology,1998,76(6):128-132.
[61]Torres-Mendez L, A., Dudek G. Statistical inference and synthesis in the image domain for mobile robot environment modeling. Intelligent Robots and Systems,IROS 2004,3(28):2699-2706.
[62]Cheling W.P, LEE C. K., Li K. C. Direct Shape from Shding with Improved Rate of Convergence, Pattern Recognition,1997,30(3):353-365.
[63]R. Zhang, P. S. Tsai, J. E. Cryer$M. Shah. Shape from Shading:A Survey, IEEE Trans. PAMI,1999,21 (8):690-705.
[64]赵冬斌,陈善本,吴林.由单目图像获得表面高度算法的分析和实现[J].计算机学报,2000,23(2)：147-152.
[65]宋丽梅,叶声华,曲兴华.基于单幅实时测量图像三维形貌恢复的研究[D].天津大学,2004.
[66]骆郴,贾亚敏,刘彤,魏鹏.利用普通扫描仪精确测量叶面积的技术及方法,北方园艺2007(5)：46-48
[67]章毓晋.图像工程[M].北京：清华大学出版社,2006
[68]Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, et al. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods[J]. Computerized Medical Imaging and Graphics,2008,32(8):720-727
[69]王海伟,陈玉成,陈建泗,等.基于形态学的图像预处理技术研究[J].微计算机信息2008,33(25)：120-125
[70]张琳,刘志勤,李亚利.基于灰度直方图的图像增强及其VC++实现[J].微计算机信息,2008,24(6)：239-243.
[71]张德丰Matlab数字图像处理[M].北京：机械工业出版社,2009.
[72]阴法名.基于OpenCV的图像处理[J].科技信息,2010.
[73]韩云生,刘国栋,刘光宇.基于Canny算子边缘特征匹配的双目深度测量[J].计算机系统应用,2009,11：52-55.
[74]张洁,檀结庆.基于各向异性扩散方程的Canny边缘检测算法[J].计算机应用,2008,28(8)：2049-2051
[75]Qinghang He, Zhenxi Zhang. A new edge detection algorithm for image corrupted by White-Gaussian noise[J]. International Journal of Electronics and Communications,2007, 61(8):546-550
[76]程培英,一种新颖的Ostu图像闽值分割方法[J].计算机应用与软件,2009,26(5)：228-231
[77]何书前,吴丽华,王建新.多分辨率二维直方图闽值分割方法[J].计算机工程与应用,2009,45(35)：182-184
[78]K. Chen, Ch. Qin. Segmentation of beef marbling based on vision threshold[J]. Computers and Electronics in Agriculture,2008,62(2):223-230
[79]S. Arora, J. Acharya, A. Verma, Prasanta K. Panigrahi. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm[J]. Pattern Recognition Letters,2008,29(2):119-125
[80]Carsten Steger, Markus Ulrich, Christian Wiedemann, Machine Vision Algorithms and Applications,2008 by Wiley-VCH
[81]李健,陈长明,廖秋筠.一种新的基于OpenCV的立体视觉三维测量方法[J].计算机测量与控制,2009,17(12)：2403-2406.
[82]黎松,平西建,丁益洪.开放源代码的计算机视觉类库OpenCv的应用[J].计算机应用与软件,2005,22(8)：134-136.
[83]石琼,沈春林,谭皓.基于OpenGL的三维建模实现方法[J].计算机工程与应用,2004,18：122-124.
[84]高嫦娥,朱劲松.三维渲染的空间网格结构OpenGL建模[J].辽宁工程技术大学学报,2002,21(3)：346-348.
[85]吴咏梅,赵敏.基于OpenGL的高效三维物体建模的研究.机械与电子,2004,10：20-22.
[86]余静,余光亮,辛永红,付亮亮.基于开源计算机视觉库OPENCV在VC中的应用.赤峰学院学报,2008,24(6)：20-21.
[87]田红鹏,马苗.OpenGL及其基于VC++6.0的开发.西安科技学院学报,2001,21(4)：365-368.
[88]Yunyoung Nam, Eenjun Hwang, Dongyoon Kim. A similarity-based leaf image retrieval scheme:joining shape and venation features[J]. Computer Vision and Image Understanding,2008,110(2):245-259.
[89]TinoDornbusch, Bruno Andrieu. Lamina2Shape-An image processing tool for an explicit description of lamina shape tested on winter wheat[J]. Computers and Electronics in Agriculture,2010,70(1):217-224.
[90]刘小川,艾矫燕,唐纪良,等.基于自适应模糊阈值的植物黑腐病叶片病斑的分割[J].中国图象图形学报,2009,14(7)：1334-1340.
[91]Joao Camargo Net.o, George E. Meyer, David D. Jones. Individual] eaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm[J]. Computers and Electronics in Agriculture,2006,51 (1-2):66-85.
[92]毛罕平,张艳诚,胡波.基于模糊C均值聚类的作物病害叶片图像分割方法研究[J].农业工程学报,2008,24(9)：136-140.
[93]孙俊.改进二维最大类间方差法及其在黄瓜目标分割中的应用[J].农业工程学报,2009,25(10)：176-181.
[94]钱丹.作物与杂草识别中图像分割影响因素的研究[D].江苏：江苏大学,2006.
[95]KristianKirk, Hans jorgen Andersen, AntonG. Thomsen, etal. Estimation of leaf area index in cereal crops using red-green images [J]. Biosystems Engineering,2009,104(3):308-317.
[96]李正明,王森,孙俊.图像分割在成熟茄子目标识别中的应用[J].农业机械学报,2009,40：105-109.
[97]张亚静,李民赞,刘刚,等.基于机器视觉和信息融合的邻接苹果分割算法[J].农业机械学报,2009,40(11)：180-183.
[98]Xiaofeng Wang, Deshuang Huang, JixiangDu, etal. Classification of plant leaf images with complicated background [J]. Applied Mathematics and Computation,2008,205(2):916-926.
[99]赵进辉,罗锡文,周志艳.基于颜色与形状特征的甘蔗病害图像分割方法[J].农业机械学报,2008,39(9)：100-104.
[100]董金勇,王建仑,李道亮,等.田间枣树叶片复杂目标图像综合分割方法研究[J].农业机械学报,2010,
[101]方江雄.基于变分水平集的图像分割方法研究：[博士]：上海交通大学,2012
[102]方玲玲.图像分割的活动轮廓模型研究：[博士]：苏州大学,2013
[103]龚永义,黄辉,于继明,关履泰.基于熵的两区域图像分割.中国图象图形学报,2011,(05)：754-760
[104]管慧娟.基于区域的图像分割方法：[硕士]：大连理工大学,2006
[105]黄长专,王彪,杨忠.图像分割方法研究.计算机技术与发展,2009,(06)：76-79+83
[106]李政国.基于区域生长法的高空间分辨率遥感图像分割与实现：[硕士]：广西大学,2008
[107]刘军伟.基于水平集的图像分割方法研究及其在医学图像中的应用：[博士]：中国科学技术大学,2009
[108]毛红达.基于区域活动轮廓模型的图像分割：[硕士]：浙江大学,2008
[109]童悍操.一个基于区域分割的图论图像分割方法的改进：[硕士]：清华大学,2012
[110]王小墨.基于区域的图像检索关键技术的研究：[硕士]：吉林大学,2006
[111]杨悦.基于模糊聚类及水平集方法的图像分割技术研究：[博士]：吉林大学,2011
[112]原野.偏微分方程图像分割模型研究：[博士]：重庆大学,2012
[113]袁建军.基于偏微分方程图像分割技术的研究：[博士]：重庆大学,2012
[114]张鲁筠.基于偏微分方程的图像分割和肿瘤生物医学建模研究：[博士]：山东大学,2013
[115]张晓梅,王诚梅,韩琥.基于区域的烟尘图像分割方法.计算机工程与应用,2008,(13)：193-195+237
[116]张燕群.基于区域生长和聚类的花卉图像分割算法研究：[硕士]：成都理工大学,2012
[117]周昌雄,于盛林.基于区域内一致性和区域间差异性的图像分割.中南大学学报(自然科学版),2005,(04)：668-672
[118]朱国普.基于活动轮廓模型的图像分割：[博士]：哈尔滨工业大学,2007
[119]陈立雪.基于直方图和脉冲耦合神经网络及边缘乘积互信息的图像分割：[硕士]：复旦大学,2012
[120]陈志辉.基于X射线图像获取和处理实现：[硕士]：吉林大学,2013
[121]郭政阳,张乐.基于边缘区域生长的图像分割.科技致富向导,2012,(36)：8
[122]何宁,张朋.基于边缘和区域信息相结合的变分水平集图像分割方法.电子学报,2009,(10)：2215-2219
[123]彭丽.基于边缘信息的阈值图像分割：[硕士]：中南大学,2009
[124]秦斌.基于边缘的血细胞图像分割技术研究：[硕士]：山东大学,2007
[125]秦绪佳,杜轶诚,张素琼,王卫红,韩军.基于边缘信息C_V模型的医学图像分割方法.小型微型计算机系统,2011,(05)：972-977
[126]唐闯.基于边缘检测的图像分割算法研究：[硕士]：燕山大学,2011
[127]徐志洁.基于边缘检测及Markov随机场模型的遥感图像分割研究：[硕士]：中国农业大学,2005
[128]杨方方.彩色图像分割技术的研究—图像边缘检测技术的研究应用：[硕士]：江南大学,2009
[129]张博.基于边缘检测的细胞图像分割方法研究与实现：[硕士]：武汉理工大学,2006
[130]毕文波.基于机器视觉的机械零件尺寸识别研究：[硕士]：山东大学,2006
[131]岑益科.基于机器视觉的鸡蛋品质检测方法研究：[硕士]：浙江大学,2006
[132]段德山.工件非接触检测中机器视觉的研究与应用：[硕士]：北京邮电大学,2007
[133]龚爱平.基于嵌入式机器视觉的信息采集与处理技术研究：[博士]：浙江大学,2013
[134]贺智.机器视觉系统在LCD行业中的应用.电子工艺技术,2007,(03)：153-156
[135]黄正福.面向尺寸检测的机器视觉系统研究：[硕士]：浙江江工业大学,2005
[136]孔峰.基于单目机器视觉的在线自动灌装系统研究：[硕士]：江苏科技大学,2012
[137]李福建,张元培.机器视觉系统组成研究.自动化博览,2004,(02)：62-64
[138]李强.基于DSP的智能车辆机器视觉系统研究：[硕士]：重庆大学,2004
[139]刘辉.RFID标签封装设备中机器视觉系统设计与实现：[硕士]：华中科技大学,2006
[140]刘金桥.基于机器视觉系统的颜色识别研究：[硕士]：新疆大学,2009
[141]刘金桥,吴金强.机器视觉系统发展及其应用.机械工程与自动化,2010,(01)：215-216
[142]刘松林.印刷品质量在线检测机器视觉系统的设计与实现：[硕士]：解放军信息工程大学,2006
[143]穆向阳,张太镒.机器视觉系统的设计.西安石油大学学报(自然科学版),2007,(06)：104-109+130
[144]盛遵冰.机器视觉图像检测与定位系统关键技术研究：[博士]：哈尔滨工业大学,2009
[145]史晋芳,苏真伟,刘小平,李强,祝钊.一种基于视觉预注意机制的选择性成像机器视觉系统.四川大学学报(工程科学版),2012,(02)：206-209
[146]史鹏飞.基于DM6437的嵌入式机器视觉系统的研究与开发：[硕士]：江南大学,2011
[147]宋学勇,赵敏.机器视觉系统的关键技术.计算机世界.2007 2007-07-23：Sect.B11,
[148]苏真伟,王瑾玮,黄明飞,关保清.不规则成像机器视觉系统探索.四川大学学报(工程科学版),2008,(06)：165-169
[149]唐珲.基于机器视觉的车道偏离预警系统的研究：[硕士]：华南理工大学,2011
[150]王伙荣.面向机器视觉系统的CCD数字相机设计：[硕士]：西安电子科技大学,2008
[151]王运哲,白雁兵,张博.机器视觉系统的设计方法.现代显示,2011,(11)：24-27
[152]文家昌.基于LabVIEW机器视觉的产品检测平台设计与应用：[硕士]：华南理工大学,2012
[153]许巧游.基于机器视觉系统的零件识别与检测的研究：[硕士]：南京航空航天大学,2006
[154]晏恒峰.基于机器视觉的激光振镜扫描系统研究：[硕士]：北京工业大学,2012
[155]俞东宝,苏真伟,晏开华.一种新型机器视觉系统及其图像矫正算法探索.激光与红外,2008,(11)：1173-1176
[156]张虎.机器视觉中二维图像的三维重建：[硕士]：北方工业大学,2006
[157]张纪明.基于PC的机器视觉系统研究.可编程控制器与工厂自动化,2006,(11)：107-110
[158]张仕玲.基于机器视觉的自动打孔机系统设计：[硕士]：成都理工大学,2012
[159]周龙.汽车发动机连杆表面缺陷机器视觉检测系统：[硕士]：浙江工业大学,2005
[160]周水琴.机器视觉系统的色度校正模型及其在西柚分级中的应用：[硕士]：浙江大学,2004
[161]吴源远(导师：张秋菊).高速分拣机械手视觉识别技术研究[硕士]：江南大学,2009
[162]杨清宇,施仁.基于因特网的工业控制网络体系结构研究,信息与控制,2002,10
[163]席亚宾,马永光,林永君.基于Internet的工业控制网络,电力系统通信,2004,12
[164]
[165]徐峥(导师：杨小健).工业生产中的远程监控技术研究,[硕士]：南京工业大学,2006
[166]黄国刚(导师：陈明).工业级嵌入式以太网络接口的实现[硕士]：西北工业大学,2005
[167]龚荣(导师：黎洪生).油井远程监测管理软件设计[硕士]：武汉理工大学,2005-06-01
[168]李超(导师：黎洪生),分布式监控系统在油田生产中的应用研究,[硕士]：武汉理工大学,2002
[169]孙正凤(导师：马宝甫).集中供热远程监控系统的组态式软件设计[硕士]：2004
[170]3G无线监控方案-例子-百度文库《互联网文档资源(http://wenku.baidu.c)》-2012-12-24
[171]Iw701FM86工业级智能Wi-Fi网关(工业wifi客户端网关)-百度文库《互联网文档资源(http://wenku.baidu.c)》-2012-08-20
[172]基于EVDO网络的视频监控方案-方案应用-中国工控网《网络(http://www.gongkong.)》-2012-01-16
[173]WCDMA 3G路由器——北京东方讯科技发展有限公司@中华工控网gkong.com《网络(http://www.gkong.com)》-2012-10-31
[174]龚爱平,吴武豪,裘正军,何勇.基于Android系统手机的叶面积测量方法,农业机械学报,2013(44)9：203-208
[175]谭峰,高艳萍.基于图像的植物叶面积无损测量方法研究,农业工程学报,2008,24(5)：170-173
[176]郭孝玉,孙玉军,王轶夫,林静媛.基于改进人工神经网络的植物叶面积测定,农业机械学报,2013,44(2)：200-204
[177]聂鹏程,杨燕,刘飞,郑金土,何勇.植物叶面积无损测量方法及仪器开发,农业工程学报2010,26(9)198、203
[178]刁军,国红,卢军,雷相东,唐守正.油松针叶面积估计模型及比叶面积的研究,[J]林业科学研究,2013,26(2)：174-180.
[179]宰松梅,温季,郭冬冬,韩启彪,邓忠,孙浩,赵东彬.基于支持向量机模型和图像处理技术的甜椒叶面积测定[J].农业工程学报,2011,27(3)：237-242
[180]李明,张长利,房俊龙.基于图像处理技术的小麦叶面积指数的提取,[J].农业工程学报201026(1)205-209.
[181]陈涛涛,迟道才,梁茜.基于矩形框几何校正的多叶面积测量方法,[J].农业工程学报,2012,28(8)：206-213.
[182]李锦卫,管鹤卿,廖桂平.基于计算机视觉的油菜叶面积计算方法研究,[J].农业网络信息,2010,12：15-18
[183]王红涛(导师：傅卫平).基于视觉的工业机器人目标识别定位方法的研究,[硕士]：西安理 工大学,2007.
[184]高永辉(导师：何文杰).图像识别技术在计算机辅助骨龄评价系统中的应用,[硕士]：河北工业大学,2003.
[185]朴磊(导师：李剑中).基于单幅图像的三维形貌恢复方法研究,[硕士]：大连理工大学,2007.
[186]寇镇淳(导师：罗钟铉).从明暗恢复形状的有理样条方法,[硕士]：大连理工大学,2005
[187]任爱民(导师：李金).基于单幅灰度图像的三维表面重构,[硕士]：哈尔滨工程大学,2006
[188]赵歆波(导师：张定华).基于分形的从明暗恢复形状方法研究,[博士]：西北工业大学,2002
[189]何理想(导师：牛连强).基于SFS方法的单幅图像的三维原型重建的研究,[硕士]：沈阳工业大学
[190]赵辉煌(导师：周德俭).焊点图像处理及焊点三维质量信息提取技术研究,[博士]：西安电子科技大学,2010
[191]满臣(导师：李剑忠).基于图像灰度的三维重构递推算法的研究,[硕士]：大连理工大学,2005
[192]陈玮(导师：李剑中).SFS三维重构递推算法的求解及精度研究,[硕士]：大连理工大学,2006
[193]黄绿娥(导师：李平康).基于机器视觉的人体运动目标智能监控系统设计与研究,[硕士]：北京交通大学,2008
[194]何建磊(导师：王建仑).单幅田间枣树叶片灰度图像三维重建算法研究,[硕士]中国农业大学,2011
[195]王述琪,刘水英.基于小波变换的红外目标图像多尺度边缘提取,[J].科技创新导报,2010,11：
[196]张晓(导师：张向东).复杂背景下的车牌定位系统设计,[硕士]：西安电子科技大学,2009
[197]尚琴(导师：王宏远).基于变化检测的视频对象分割方法研究,[硕士]：华中科技大学2007
[198]陈秋芳(导师：王建国).数学形态学在SAR图像处理中的应用研究,[硕士]：电子科技大学论文,2008
[200]李聪(导师：张明新).基于区域综合特征的图像检索,[硕士]：西北师范大学论文,2009
[201]张汝楠,孙丽萍.基于X-ray CT技术原木三维重建检测缺陷方法的初步探讨,[J].森林工程,2008,9：
[202]刘磊(导师：张大卫).基于计算机视觉技术的汽车涂膜缺陷检测方法的研究,[硕士]：天 津大学,2008
[203]申川(导师：蒋焕煜).基于双目立体视觉系统的设施农业作物位置信息获取,[硕士]：浙江大学
[204]刘战涛(导师：李琦,刘龙).基于DSP的带钢表面缺陷检测系统的软件设计与实现,[硕士]：西安理工大学论文,2009
[205]冯建强(导师：刘文波).人脸分割和特征提取技术研究,[硕士]：南京航空航天大学,2004
[206]徐欢(导师：陆尧胜).宫颈细胞涂片显微图像拼接系统的研究与实现,[硕士]：暨南大学,2008
[207]黄隆华,陈志辉,彭小宁,叶青,王正友.一种离焦模糊图像边缘检测新方法,[J].计算技术与自动化,2010,6：
[208]朱秋楠,李云峰,邓刚桥,朱秋柯,张现军.基于膨胀和Hough变换的焊缝位置检测方法,[J].工具技术,2011,11：
[209]高贵朋(导师：赵国良).虹膜识别算法的研究,[硕士]：哈尔滨工程大学,2004
[210]吕小军(导师：夏胜平).基于感兴趣区域特征索引的图像检索研究,[硕士]：国防科学技术大学,2006
[211]姜东海(导师：王殊).基于图像处理的火灾烟雾识别技术研究,[硕士]：华中科技大学,2006
[212]王刚(导师：李文辉).基于分割的医学图像检索技术研究与实现,[硕士]：吉林大学,2008
[213]杨佳颖(导师：王钲旋).基于内容的医学图像检索系统,[硕士]：吉林大学,2005
[214]岁永兴(导师：于明).面向医学应用的纹理图像分割方法研究,[硕士]：河北工业大学,2004
[215]夏营威,张龙,董浩,荆熠,刘勇.透明BOPP薄膜耐磨性能与材料成分关系的实验方法,[J].仪器仪表学报,2012,6：
[216]杨智楠(导师：卢奕南).基于形状的医学图像检索,[硕士]：吉林大学,2006
[217]赵媛媛,童隆正,江桂莲,于春水.基于MRI的多发性硬化患者和阿尔茨海默病患者纹理特征对比,[J].首都医科大学学报,2009,4：
[218]徐坚(导师：王志坚).基于内容的卫星云图检索系统设计与实现,[硕士]：河海大学,2004
[219]王李冬,邰晓英,巴特尔,赵杰煜.综合纹理特征和语义信息的医学图像检索,[J].计算机应用,2005,10：
[220]胡清平,张晓晖.基于熵空间理论和小波变换的红外图像纹理分割,[J].红外技术,2013,8：
[221]王红君,陈伟,赵辉,岳友军.复杂背景下植物叶片的彩色图像分割中国农机化学报,2013,34(2)：207-212
[222]方宏,王仲.基于复杂背景的图像分割算法,2006,20(2)：77-81
[223]王萍,王立地.自然场景下植物叶片图像分割方法研究,农业科技与装备,2011,200(2)：40-43
[224]熊琴(导师：王旭东).粗集料形状特征的数字图像分析,[硕士]：重庆交通大学,2011
[2]TinoDornbusch, Bruno Andrieu, Lamina2Shape-An image processing tool for an exp] icit description of lamina shape tested on winter wheat, Computers and Electronics in Agriculture, 2010,70(1):217-224.
[3]Liu Xiaochuan, AiJiaoyan, TangJiliang, et al. The Segmentation of Black Rot Lesion of Cruciferous Plant Based on Self-adaptive Fuzzy Threshold, Journal of Image and Graphics, 2009,14(7):1334-1340.
[4]Joao Camargo Neto, George E. Meyer, David D. Jones, Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm, Computers and Electronics in Agriculture,2006,51(1-2):66-85.
[5]Mao Hanping, ZhangYancheng, Hu Bo, Segmentation of crop disease leaf images using fuzzy C-means clustering algorithm, Transactions of the Chinese Society of Agricultural Engineering,2008,24(9):136-140.
[6]Sun Jun, Improved 2D maximum between-cluster variance algorithm and its application to cucumber target segmentation, Transactions of the CSAE,2009,25(10):176-181.
[7]Qian Dan, Research on Influencing Factors of Image Segmentation for Crop and Weed Identification[D], Jiangsu:Jiangsu University,2006.
[8]KristianKirk, Hans Jorgen Andersen, AntonG. Thomsen, et al. Estimation of leaf area index in cereal crops using red-green images, Biosystems Engineering,2009,104(3):308-317.
[9]Li Zhengming, WangSen, Sun Jun, Image Segmentation in Object Recognition of Mature Eggplant, Transactions of the Chinese Society of Agricultural Machinery,2009,40:105-109.
[10]Zhang Yajing, LiMinzan, LiuGang, et al. Separating Adjoined Apples Based on Machine Vision and Information Fusion, Transactions of the Chinese Society of Agricultural Machinery,2009,40(11):180-183.
[11]Xiaofeng Wang, Deshuang Huang, JixiangDu, et al. Classification of plant leaf images with complicated background, Applied Mathematics and Computation,2008, 205(2):916-926.
[12]Zhao Jinhui, LuoXiwen, ZhouZhiyan, Image Segmentation Method for Sugarcane. Diseases Based on Color and Shape Features, Transactions of the Chinese Society of Agricultural Machinery,2008,39(9):100-104.
[13]Dong Jinyong, Wang Jianlun, Li Daoliang, et al. Research on Complex Target Image of Field Jujube Leaf Segmentation Based on Integrated Technology, Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, v 42, n 1, pp.165-170, January 2011
[14]D. He, Y. Bai, Y. Wang, H. Wu, A Crop Field Remote Monitoring System Based on Web-Server-Embedded Technology and CDMA Service, in Proc. SAINT Workshops,2007.
[15]Qinghang He, Zhenxi Zhang, A new edge detection algorithm for image corrupted by White-Gaussian noise, International Journal of Electronics and Communications,2007,61(8): 546-550
[16]Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, et al. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods, Computerized Medical Imaging and Graphics,2008,32(8):720-727
[17]K. Chen, Ch. Qin. Segmentation of beef marbling based on vision threshold, Computers and Electronics in Agriculture,2008,62(2):223-230
[18]S. Arora, J. Acharya, A. Verma, Prasanta K. Panigrahi, Multilevel thresholding for image segmentation through a fast statistical recursive algorithm, Pattern Recognition Letters,2008,29(2):119-125
[19]Marco A. G. de Carvalho, Roberto de A. Lotufo, Michel Couprie, Morphological segmentation of yeast by image analysis, Image and Vision Computing,2007,25(6):34-39
[20]Kamal Hammouche, Moussa Diaf, Patrick Siarry. A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation, Computer Vision and Image Understanding,2008,109(2):163-175.
[21]S. Arora, J. Acharya, A. Verma, Prasanta K. Panigrahi, Multilevel thresholding for image segmentation through a fast statistical recursive algorithm, Pattern Recognition Letters,2008,29(2):119-125.
[22]Jifeng Ninga, Lei Zhang, David Zhang, Chengke Wu, Interactive image segmentation by maximal similarity based region merging, Pattern Recognition,2009.03.004.
[23]Maria Frucci, Giuliana Ramella, Gabriella Sanniti di Bajam Using resolution pyramids for watershed image segmentation, Image and Vision Computing,2007, 25(6):1021-1031.
[24]Ardeshir Goshtasby, Piecewise cubic mapping functions for image registration, Pattern Recognition, Volume 20, Issue 5,1987, pp.525-533.
[25]S. Anna Durai, and E. Anna Saro, Image Mapping with Cumulative Distribution Function for Quick Convergence of Counter Propagation Neural Networks in Image Compression, World Academy of Science, Engineering and Technology 16,2006, pp.43-48.
[26]Lei Huang; Linrang Zhang; Shunjun Wu; A new method for noise subspace estimation based on the spatial smoothing Lanczos algorithm, Antennas and Propagation Society International Symposium,2004. IEEE,20-25 June 2004 pp.3689-3692 Vol.4
[27]P. E. Wellstead, G. R. Wagner, J. R. Caldas-Pinto, Two-dimensional adaptive prediction, smoothing and filtering, IEE PROCEEDINGS, Vol.134, Pt. F, No.3, JUNE 1987
[28]Ming-Kuei Hu. Visual pattern recognition by moment invariants [J]. Information Theory, IRE Transactions on,1962,8(2):179-187.
[29]Dingmingyue, Changjinling, Pengjiaxiong. Research on moment invariant, algorithm [J].Journal of data acquisition and processing,1992,7(1):1-9.
[30]Huangyong, Wangchongjun, Wangliang, Hangyan, Chenchaoqian. Research on algorithm of image retrieval based on shape moment invariants [J]. Computer Application Research, 2004,21(7):256-257.
[31]于峰等.一种基于图像特征值算法的叶面积测定方法.中国农业大学学报,2007(4)：67-69
[32]Yud-Ren Chen, Kuanglin Chao, Moon S. Kim. Machine vision technology for agricultural applications [J]. Computers and Electronics in Agriculture,2002,36(2) 173-191.
[33]B. K. P. Horn. Shape from Shading:A method for Obtaining the Shape of a Smooth Opaque object from One View[J]. Ph. D.. dissertation:Massachusettes Institute of Technology.1970,9(1):129-134.
[34]A Bruckstein. on Shape from Shading[J]. CVGIP,1987 44:139-154.
[35]S. Trandri, YH. Yang. Comparison of two shape-from-shading algorithms[J]. Pattern Recognition Letter,1990,11:637-642
[36]E. Rouy, A. Tourin. A Viscosity Solutions Approach to Shape from Shading[J]. SIAM J. Numberical Alnalysis,1992,29(3):867-884.
[37]J.Oliensis. Shape from Shading as s Partially Well-Constrained Problem[J].CVGIP,1991,S4:163-183.
[38]P. Dupuis, J. Oliensis. Direct Method for Reconstructing Shape from Shading[J]. IEEE Proc. Computer Vision and Pattern Recognition,1992,453-458.
[39]J. Oliensis, P. Dupuis. A Global Algorithm for Shape from Shad ing [J]. In:Proc. Int'1 Conf. Computer Vision,1993,692-701.
[40]M. Bichsel, A. P. Pentland. A Simple Algorithm for Shape from Shading[J]. IEEE Proc. Computer Vision and Pattern Recognition[J],1992,459-465.
[41]R. Kimmel, A. M. Bruckstein. Shape from Shading via Level Sets[J]. Israel Inst. Of Technology,1992, CIS Report #9209.
[42]K. lkeuchi, B.Horn. Numerical Shape from Shading and Occluding Boundaries[J]. Artificial Intelligence,1981,17:141-184.
[43]B. K. P. Horn, M. J. Brooks. The Variational Approach to Shape from Shading[J].CVGIP,1986,33:174-208.
[44]R. T. Frankot, R. Chellapa. A Method for Enforcing Integrability in Shape from Shading Algorithms[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1988,10:439-451.
[45]M. J. Brooks, B. K. P. Horn. Shape and Shading Algorithms [J]. In:Proc. Int'1 Joint Conf. on Artificial Intelligence,1985,932-936.
[46]S. Bakshi. A Study of Shape from Shading Algorithms[J]. Proc. the Fifth Annual Graduate Symposium Computational Science,1993,214-225.
[47]B. K. P. Horn. Height and Gradient from Shading[J]. Int's J. Computer Vision,1989,37-75.
[48]R. Szeliski. Fast Shape from Shading[J].CVGIP,1991,53:129-153.
[49]0. E. Vega, YH. Yang. Shaping Logic:A Heuristic Approch to Recover Shape from Shading[J]. IEEE Trans:Parttern Analysis and Machine Intelligence,1993,15(6):592-597.
[50]Q. Zheng, R. Chellppa. Estimating of Illumimamt Direction, Albedo, and Shape from Shading[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1991,13(7):680-702.
[51]Y. G. Lecelerc, A. F. Boick. The Direct Computation of Height from Shading[J]. IEEE Proc. Computer Vision and Pattern Recognition,1991,552-558.
[52]K. M. Lee, C. C. J. Kuo. Shape from Shading with a Linear Trangular Elemrnt Surface Model[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1993,15(8):815-882.
[53]A. P. Pentland. Local Shading Analysis[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1993,15(8):815-882.
[54]C. H. Lee and A. Rosenfeld. Improved Methods of Estimating Shape from Shading Using the Light Source Coordinate System[J]. Artificial Intelligence,1985,26:125-143.
[55]F. Ferrie, M. Levine. Where and Why Local Shading Analysis Work[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1989,11:198-206.
[56]A. P. Pentland. Liener Shape from Shading[J]. Computer Vision,1990,4(2):153-162.
[57]K. Ikenchi, B.Horn. Numerical Shape from Shading and Occluding Boundaries[J]. Artificial Intelligence,1981,17(8):141-184.
[58]B.T.Phong. Illumination for Computer Generated Images[J]. Ph. D. Dissertation:University of Utah,1973(7):102-106.
[59]'J.F. Blinn. Model of Light Reflection for Computer Synthesiszed Pictures[J]. Computer Graphics,1977,11(2):192-198.
[60]Werner A. Skalski K. Reverse Engineering of Free from Surfaces. Materials Processing Technology,1998,76(6):128-132.
[61]Torres-Mendez L, A., Dudek G. Statistical inference and synthesis in the image domain for mobile robot environment modeling. Intelligent Robots and Systems,IROS 2004,3(28):2699-2706.
[62]Cheling W.P, LEE C. K., Li K. C. Direct Shape from Shding with Improved Rate of Convergence, Pattern Recognition,1997,30(3):353-365.
[63]R. Zhang, P. S. Tsai, J. E. Cryer$M. Shah. Shape from Shading:A Survey, IEEE Trans. PAMI,1999,21 (8):690-705.
[64]赵冬斌,陈善本,吴林.由单目图像获得表面高度算法的分析和实现[J].计算机学报,2000,23(2)：147-152.
[65]宋丽梅,叶声华,曲兴华.基于单幅实时测量图像三维形貌恢复的研究[D].天津大学,2004.
[66]骆郴,贾亚敏,刘彤,魏鹏.利用普通扫描仪精确测量叶面积的技术及方法,北方园艺2007(5)：46-48
[67]章毓晋.图像工程[M].北京：清华大学出版社,2006
[68]Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, et al. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods[J]. Computerized Medical Imaging and Graphics,2008,32(8):720-727
[69]王海伟,陈玉成,陈建泗,等.基于形态学的图像预处理技术研究[J].微计算机信息2008,33(25)：120-125
[70]张琳,刘志勤,李亚利.基于灰度直方图的图像增强及其VC++实现[J].微计算机信息,2008,24(6)：239-243.
[71]张德丰Matlab数字图像处理[M].北京：机械工业出版社,2009.
[72]阴法名.基于OpenCV的图像处理[J].科技信息,2010.
[73]韩云生,刘国栋,刘光宇.基于Canny算子边缘特征匹配的双目深度测量[J].计算机系统应用,2009,11：52-55.
[74]张洁,檀结庆.基于各向异性扩散方程的Canny边缘检测算法[J].计算机应用,2008,28(8)：2049-2051
[75]Qinghang He, Zhenxi Zhang. A new edge detection algorithm for image corrupted by White-Gaussian noise[J]. International Journal of Electronics and Communications,2007, 61(8):546-550
[76]程培英,一种新颖的Ostu图像闽值分割方法[J].计算机应用与软件,2009,26(5)：228-231
[77]何书前,吴丽华,王建新.多分辨率二维直方图闽值分割方法[J].计算机工程与应用,2009,45(35)：182-184
[78]K. Chen, Ch. Qin. Segmentation of beef marbling based on vision threshold[J]. Computers and Electronics in Agriculture,2008,62(2):223-230
[79]S. Arora, J. Acharya, A. Verma, Prasanta K. Panigrahi. Multilevel thresholding for image segmentation through a fast statistical recursive algorithm[J]. Pattern Recognition Letters,2008,29(2):119-125
[80]Carsten Steger, Markus Ulrich, Christian Wiedemann, Machine Vision Algorithms and Applications,2008 by Wiley-VCH
[81]李健,陈长明,廖秋筠.一种新的基于OpenCV的立体视觉三维测量方法[J].计算机测量与控制,2009,17(12)：2403-2406.
[82]黎松,平西建,丁益洪.开放源代码的计算机视觉类库OpenCv的应用[J].计算机应用与软件,2005,22(8)：134-136.
[83]石琼,沈春林,谭皓.基于OpenGL的三维建模实现方法[J].计算机工程与应用,2004,18：122-124.
[84]高嫦娥,朱劲松.三维渲染的空间网格结构OpenGL建模[J].辽宁工程技术大学学报,2002,21(3)：346-348.
[85]吴咏梅,赵敏.基于OpenGL的高效三维物体建模的研究.机械与电子,2004,10：20-22.
[86]余静,余光亮,辛永红,付亮亮.基于开源计算机视觉库OPENCV在VC中的应用.赤峰学院学报,2008,24(6)：20-21.
[87]田红鹏,马苗.OpenGL及其基于VC++6.0的开发.西安科技学院学报,2001,21(4)：365-368.
[88]Yunyoung Nam, Eenjun Hwang, Dongyoon Kim. A similarity-based leaf image retrieval scheme:joining shape and venation features[J]. Computer Vision and Image Understanding,2008,110(2):245-259.
[89]TinoDornbusch, Bruno Andrieu. Lamina2Shape-An image processing tool for an explicit description of lamina shape tested on winter wheat[J]. Computers and Electronics in Agriculture,2010,70(1):217-224.
[90]刘小川,艾矫燕,唐纪良,等.基于自适应模糊阈值的植物黑腐病叶片病斑的分割[J].中国图象图形学报,2009,14(7)：1334-1340.
[91]Joao Camargo Net.o, George E. Meyer, David D. Jones. Individual] eaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm[J]. Computers and Electronics in Agriculture,2006,51 (1-2):66-85.
[92]毛罕平,张艳诚,胡波.基于模糊C均值聚类的作物病害叶片图像分割方法研究[J].农业工程学报,2008,24(9)：136-140.
[93]孙俊.改进二维最大类间方差法及其在黄瓜目标分割中的应用[J].农业工程学报,2009,25(10)：176-181.
[94]钱丹.作物与杂草识别中图像分割影响因素的研究[D].江苏：江苏大学,2006.
[95]KristianKirk, Hans jorgen Andersen, AntonG. Thomsen, etal. Estimation of leaf area index in cereal crops using red-green images [J]. Biosystems Engineering,2009,104(3):308-317.
[96]李正明,王森,孙俊.图像分割在成熟茄子目标识别中的应用[J].农业机械学报,2009,40：105-109.
[97]张亚静,李民赞,刘刚,等.基于机器视觉和信息融合的邻接苹果分割算法[J].农业机械学报,2009,40(11)：180-183.
[98]Xiaofeng Wang, Deshuang Huang, JixiangDu, etal. Classification of plant leaf images with complicated background [J]. Applied Mathematics and Computation,2008,205(2):916-926.
[99]赵进辉,罗锡文,周志艳.基于颜色与形状特征的甘蔗病害图像分割方法[J].农业机械学报,2008,39(9)：100-104.
[100]董金勇,王建仑,李道亮,等.田间枣树叶片复杂目标图像综合分割方法研究[J].农业机械学报,2010,
[101]方江雄.基于变分水平集的图像分割方法研究：[博士]：上海交通大学,2012
[102]方玲玲.图像分割的活动轮廓模型研究：[博士]：苏州大学,2013
[103]龚永义,黄辉,于继明,关履泰.基于熵的两区域图像分割.中国图象图形学报,2011,(05)：754-760
[104]管慧娟.基于区域的图像分割方法：[硕士]：大连理工大学,2006
[105]黄长专,王彪,杨忠.图像分割方法研究.计算机技术与发展,2009,(06)：76-79+83
[106]李政国.基于区域生长法的高空间分辨率遥感图像分割与实现：[硕士]：广西大学,2008
[107]刘军伟.基于水平集的图像分割方法研究及其在医学图像中的应用：[博士]：中国科学技术大学,2009
[108]毛红达.基于区域活动轮廓模型的图像分割：[硕士]：浙江大学,2008
[109]童悍操.一个基于区域分割的图论图像分割方法的改进：[硕士]：清华大学,2012
[110]王小墨.基于区域的图像检索关键技术的研究：[硕士]：吉林大学,2006
[111]杨悦.基于模糊聚类及水平集方法的图像分割技术研究：[博士]：吉林大学,2011
[112]原野.偏微分方程图像分割模型研究：[博士]：重庆大学,2012
[113]袁建军.基于偏微分方程图像分割技术的研究：[博士]：重庆大学,2012
[114]张鲁筠.基于偏微分方程的图像分割和肿瘤生物医学建模研究：[博士]：山东大学,2013
[115]张晓梅,王诚梅,韩琥.基于区域的烟尘图像分割方法.计算机工程与应用,2008,(13)：193-195+237
[116]张燕群.基于区域生长和聚类的花卉图像分割算法研究：[硕士]：成都理工大学,2012
[117]周昌雄,于盛林.基于区域内一致性和区域间差异性的图像分割.中南大学学报(自然科学版),2005,(04)：668-672
[118]朱国普.基于活动轮廓模型的图像分割：[博士]：哈尔滨工业大学,2007
[119]陈立雪.基于直方图和脉冲耦合神经网络及边缘乘积互信息的图像分割：[硕士]：复旦大学,2012
[120]陈志辉.基于X射线图像获取和处理实现：[硕士]：吉林大学,2013
[121]郭政阳,张乐.基于边缘区域生长的图像分割.科技致富向导,2012,(36)：8
[122]何宁,张朋.基于边缘和区域信息相结合的变分水平集图像分割方法.电子学报,2009,(10)：2215-2219
[123]彭丽.基于边缘信息的阈值图像分割：[硕士]：中南大学,2009
[124]秦斌.基于边缘的血细胞图像分割技术研究：[硕士]：山东大学,2007
[125]秦绪佳,杜轶诚,张素琼,王卫红,韩军.基于边缘信息C_V模型的医学图像分割方法.小型微型计算机系统,2011,(05)：972-977
[126]唐闯.基于边缘检测的图像分割算法研究：[硕士]：燕山大学,2011
[127]徐志洁.基于边缘检测及Markov随机场模型的遥感图像分割研究：[硕士]：中国农业大学,2005
[128]杨方方.彩色图像分割技术的研究—图像边缘检测技术的研究应用：[硕士]：江南大学,2009
[129]张博.基于边缘检测的细胞图像分割方法研究与实现：[硕士]：武汉理工大学,2006
[130]毕文波.基于机器视觉的机械零件尺寸识别研究：[硕士]：山东大学,2006
[131]岑益科.基于机器视觉的鸡蛋品质检测方法研究：[硕士]：浙江大学,2006
[132]段德山.工件非接触检测中机器视觉的研究与应用：[硕士]：北京邮电大学,2007
[133]龚爱平.基于嵌入式机器视觉的信息采集与处理技术研究：[博士]：浙江大学,2013
[134]贺智.机器视觉系统在LCD行业中的应用.电子工艺技术,2007,(03)：153-156
[135]黄正福.面向尺寸检测的机器视觉系统研究：[硕士]：浙江江工业大学,2005
[136]孔峰.基于单目机器视觉的在线自动灌装系统研究：[硕士]：江苏科技大学,2012
[137]李福建,张元培.机器视觉系统组成研究.自动化博览,2004,(02)：62-64
[138]李强.基于DSP的智能车辆机器视觉系统研究：[硕士]：重庆大学,2004
[139]刘辉.RFID标签封装设备中机器视觉系统设计与实现：[硕士]：华中科技大学,2006
[140]刘金桥.基于机器视觉系统的颜色识别研究：[硕士]：新疆大学,2009
[141]刘金桥,吴金强.机器视觉系统发展及其应用.机械工程与自动化,2010,(01)：215-216
[142]刘松林.印刷品质量在线检测机器视觉系统的设计与实现：[硕士]：解放军信息工程大学,2006
[143]穆向阳,张太镒.机器视觉系统的设计.西安石油大学学报(自然科学版),2007,(06)：104-109+130
[144]盛遵冰.机器视觉图像检测与定位系统关键技术研究：[博士]：哈尔滨工业大学,2009
[145]史晋芳,苏真伟,刘小平,李强,祝钊.一种基于视觉预注意机制的选择性成像机器视觉系统.四川大学学报(工程科学版),2012,(02)：206-209
[146]史鹏飞.基于DM6437的嵌入式机器视觉系统的研究与开发：[硕士]：江南大学,2011
[147]宋学勇,赵敏.机器视觉系统的关键技术.计算机世界.2007 2007-07-23：Sect.B11,
[148]苏真伟,王瑾玮,黄明飞,关保清.不规则成像机器视觉系统探索.四川大学学报(工程科学版),2008,(06)：165-169
[149]唐珲.基于机器视觉的车道偏离预警系统的研究：[硕士]：华南理工大学,2011
[150]王伙荣.面向机器视觉系统的CCD数字相机设计：[硕士]：西安电子科技大学,2008
[151]王运哲,白雁兵,张博.机器视觉系统的设计方法.现代显示,2011,(11)：24-27
[152]文家昌.基于LabVIEW机器视觉的产品检测平台设计与应用：[硕士]：华南理工大学,2012
[153]许巧游.基于机器视觉系统的零件识别与检测的研究：[硕士]：南京航空航天大学,2006
[154]晏恒峰.基于机器视觉的激光振镜扫描系统研究：[硕士]：北京工业大学,2012
[155]俞东宝,苏真伟,晏开华.一种新型机器视觉系统及其图像矫正算法探索.激光与红外,2008,(11)：1173-1176
[156]张虎.机器视觉中二维图像的三维重建：[硕士]：北方工业大学,2006
[157]张纪明.基于PC的机器视觉系统研究.可编程控制器与工厂自动化,2006,(11)：107-110
[158]张仕玲.基于机器视觉的自动打孔机系统设计：[硕士]：成都理工大学,2012
[159]周龙.汽车发动机连杆表面缺陷机器视觉检测系统：[硕士]：浙江工业大学,2005
[160]周水琴.机器视觉系统的色度校正模型及其在西柚分级中的应用：[硕士]：浙江大学,2004
[161]吴源远(导师：张秋菊).高速分拣机械手视觉识别技术研究[硕士]：江南大学,2009
[162]杨清宇,施仁.基于因特网的工业控制网络体系结构研究,信息与控制,2002,10
[163]席亚宾,马永光,林永君.基于Internet的工业控制网络,电力系统通信,2004,12
[164]
[165]徐峥(导师：杨小健).工业生产中的远程监控技术研究,[硕士]：南京工业大学,2006
[166]黄国刚(导师：陈明).工业级嵌入式以太网络接口的实现[硕士]：西北工业大学,2005
[167]龚荣(导师：黎洪生).油井远程监测管理软件设计[硕士]：武汉理工大学,2005-06-01
[168]李超(导师：黎洪生),分布式监控系统在油田生产中的应用研究,[硕士]：武汉理工大学,2002
[169]孙正凤(导师：马宝甫).集中供热远程监控系统的组态式软件设计[硕士]：2004
[170]3G无线监控方案-例子-百度文库《互联网文档资源(http://wenku.baidu.c)》-2012-12-24
[171]Iw701FM86工业级智能Wi-Fi网关(工业wifi客户端网关)-百度文库《互联网文档资源(http://wenku.baidu.c)》-2012-08-20
[172]基于EVDO网络的视频监控方案-方案应用-中国工控网《网络(http://www.gongkong.)》-2012-01-16
[173]WCDMA 3G路由器——北京东方讯科技发展有限公司@中华工控网gkong.com《网络(http://www.gkong.com)》-2012-10-31
[174]龚爱平,吴武豪,裘正军,何勇.基于Android系统手机的叶面积测量方法,农业机械学报,2013(44)9：203-208
[175]谭峰,高艳萍.基于图像的植物叶面积无损测量方法研究,农业工程学报,2008,24(5)：170-173
[176]郭孝玉,孙玉军,王轶夫,林静媛.基于改进人工神经网络的植物叶面积测定,农业机械学报,2013,44(2)：200-204
[177]聂鹏程,杨燕,刘飞,郑金土,何勇.植物叶面积无损测量方法及仪器开发,农业工程学报2010,26(9)198、203
[178]刁军,国红,卢军,雷相东,唐守正.油松针叶面积估计模型及比叶面积的研究,[J]林业科学研究,2013,26(2)：174-180.
[179]宰松梅,温季,郭冬冬,韩启彪,邓忠,孙浩,赵东彬.基于支持向量机模型和图像处理技术的甜椒叶面积测定[J].农业工程学报,2011,27(3)：237-242
[180]李明,张长利,房俊龙.基于图像处理技术的小麦叶面积指数的提取,[J].农业工程学报201026(1)205-209.
[181]陈涛涛,迟道才,梁茜.基于矩形框几何校正的多叶面积测量方法,[J].农业工程学报,2012,28(8)：206-213.
[182]李锦卫,管鹤卿,廖桂平.基于计算机视觉的油菜叶面积计算方法研究,[J].农业网络信息,2010,12：15-18
[183]王红涛(导师：傅卫平).基于视觉的工业机器人目标识别定位方法的研究,[硕士]：西安理 工大学,2007.
[184]高永辉(导师：何文杰).图像识别技术在计算机辅助骨龄评价系统中的应用,[硕士]：河北工业大学,2003.
[185]朴磊(导师：李剑中).基于单幅图像的三维形貌恢复方法研究,[硕士]：大连理工大学,2007.
[186]寇镇淳(导师：罗钟铉).从明暗恢复形状的有理样条方法,[硕士]：大连理工大学,2005
[187]任爱民(导师：李金).基于单幅灰度图像的三维表面重构,[硕士]：哈尔滨工程大学,2006
[188]赵歆波(导师：张定华).基于分形的从明暗恢复形状方法研究,[博士]：西北工业大学,2002
[189]何理想(导师：牛连强).基于SFS方法的单幅图像的三维原型重建的研究,[硕士]：沈阳工业大学
[190]赵辉煌(导师：周德俭).焊点图像处理及焊点三维质量信息提取技术研究,[博士]：西安电子科技大学,2010
[191]满臣(导师：李剑忠).基于图像灰度的三维重构递推算法的研究,[硕士]：大连理工大学,2005
[192]陈玮(导师：李剑中).SFS三维重构递推算法的求解及精度研究,[硕士]：大连理工大学,2006
[193]黄绿娥(导师：李平康).基于机器视觉的人体运动目标智能监控系统设计与研究,[硕士]：北京交通大学,2008
[194]何建磊(导师：王建仑).单幅田间枣树叶片灰度图像三维重建算法研究,[硕士]中国农业大学,2011
[195]王述琪,刘水英.基于小波变换的红外目标图像多尺度边缘提取,[J].科技创新导报,2010,11：
[196]张晓(导师：张向东).复杂背景下的车牌定位系统设计,[硕士]：西安电子科技大学,2009
[197]尚琴(导师：王宏远).基于变化检测的视频对象分割方法研究,[硕士]：华中科技大学2007
[198]陈秋芳(导师：王建国).数学形态学在SAR图像处理中的应用研究,[硕士]：电子科技大学论文,2008
[200]李聪(导师：张明新).基于区域综合特征的图像检索,[硕士]：西北师范大学论文,2009
[201]张汝楠,孙丽萍.基于X-ray CT技术原木三维重建检测缺陷方法的初步探讨,[J].森林工程,2008,9：
[202]刘磊(导师：张大卫).基于计算机视觉技术的汽车涂膜缺陷检测方法的研究,[硕士]：天 津大学,2008
[203]申川(导师：蒋焕煜).基于双目立体视觉系统的设施农业作物位置信息获取,[硕士]：浙江大学
[204]刘战涛(导师：李琦,刘龙).基于DSP的带钢表面缺陷检测系统的软件设计与实现,[硕士]：西安理工大学论文,2009
[205]冯建强(导师：刘文波).人脸分割和特征提取技术研究,[硕士]：南京航空航天大学,2004
[206]徐欢(导师：陆尧胜).宫颈细胞涂片显微图像拼接系统的研究与实现,[硕士]：暨南大学,2008
[207]黄隆华,陈志辉,彭小宁,叶青,王正友.一种离焦模糊图像边缘检测新方法,[J].计算技术与自动化,2010,6：
[208]朱秋楠,李云峰,邓刚桥,朱秋柯,张现军.基于膨胀和Hough变换的焊缝位置检测方法,[J].工具技术,2011,11：
[209]高贵朋(导师：赵国良).虹膜识别算法的研究,[硕士]：哈尔滨工程大学,2004
[210]吕小军(导师：夏胜平).基于感兴趣区域特征索引的图像检索研究,[硕士]：国防科学技术大学,2006
[211]姜东海(导师：王殊).基于图像处理的火灾烟雾识别技术研究,[硕士]：华中科技大学,2006
[212]王刚(导师：李文辉).基于分割的医学图像检索技术研究与实现,[硕士]：吉林大学,2008
[213]杨佳颖(导师：王钲旋).基于内容的医学图像检索系统,[硕士]：吉林大学,2005
[214]岁永兴(导师：于明).面向医学应用的纹理图像分割方法研究,[硕士]：河北工业大学,2004
[215]夏营威,张龙,董浩,荆熠,刘勇.透明BOPP薄膜耐磨性能与材料成分关系的实验方法,[J].仪器仪表学报,2012,6：
[216]杨智楠(导师：卢奕南).基于形状的医学图像检索,[硕士]：吉林大学,2006
[217]赵媛媛,童隆正,江桂莲,于春水.基于MRI的多发性硬化患者和阿尔茨海默病患者纹理特征对比,[J].首都医科大学学报,2009,4：
[218]徐坚(导师：王志坚).基于内容的卫星云图检索系统设计与实现,[硕士]：河海大学,2004
[219]王李冬,邰晓英,巴特尔,赵杰煜.综合纹理特征和语义信息的医学图像检索,[J].计算机应用,2005,10：
[220]胡清平,张晓晖.基于熵空间理论和小波变换的红外图像纹理分割,[J].红外技术,2013,8：
[221]王红君,陈伟,赵辉,岳友军.复杂背景下植物叶片的彩色图像分割中国农机化学报,2013,34(2)：207-212
[222]方宏,王仲.基于复杂背景的图像分割算法,2006,20(2)：77-81
[223]王萍,王立地.自然场景下植物叶片图像分割方法研究,农业科技与装备,2011,200(2)：40-43
[224]熊琴(导师：王旭东).粗集料形状特征的数字图像分析,[硕士]：重庆交通大学,2011
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |