蓝莓机器视觉识别与标定技术研究
|
摘要
蓝莓采摘机器人是一种能够在野外和林区连续、实时进行蓝莓采摘的智能机器人系统,其研究涉及多个学科的理论与技术,体现了林业现代化的最新成果,具有重要的研究意义和应用价值,受到了世界各国的重视。视觉引导下与采摘机械臂的定位是蓝莓采摘机器人研究的重要关键技术之一。视觉系统的主要功能是对蓝莓的检测识别与定位,从而确定采摘机械系统的路径规划,实现采摘机器人的智能化操作。立体视觉系统是一种被动式的传感方式,具有获取的有用信息丰富、价格低廉、质量可靠等优点。
本论文旨在对蓝莓采摘机器人的基于双目视觉系统的立体视觉检测及定位系统进行研究。立体视觉系统的精确定位是立体视觉系统研究的基础。与实验室环境不同,蓝莓采摘机器人的主要应用背景为林区和野外,所以要求摄像机的定标过程应快速、精确,且必须是在线标定。围绕这个中心,本文提出一种快速精确的定标方案,该方案环境适应性强,只需要在车辆手臂任意位置处标记几个点,让手臂在视觉系统中运动几个位置,即可完成整个在线标定工作。与传统方法相比,本文提出的方法具有操作简单,精度高等优点。
本文对于立体匹配及手眼标定等算法进行了深入研究。立体匹配以提高实时性和精度为目标,利用反向投影方法实现了机器人视觉系统的快速匹配。本文提出的方法在定性、定量分析效果上都优于传统模板匹配方法。通过立体定位的实验研究与验证,实现了将手眼定标系统与立体定位的协调统一。
根据蓝莓采摘机器人对立体视觉系统的计算量、智能化水平、实时性要求,本文提出了基于视觉信息的蓝莓检测方法。包括基于颜色信息结合人工神经元网络模型的蓝莓分割方法和基于形状信息的蓝莓检测方法,并进行了实验验证。阐述了利用视觉技术,采用矩描述方法,根据蓝莓形状及尺寸进行蓝莓分级研究及实验验证。
通过综合实验表明,本文提出的基于视觉系统的蓝莓检测、定位及分级方法具有较高的实用和理论价值。
Blueberry picker is an intelligent robot, which can run autonomously and continuously on outside and hills. Its research, which involves theories and technologies of many aspects, and embodies the latest achievements of information and artificial intelligence, is of great value in research and application. In recent years, all fruit pickers and harvest robots in agriculture and forestry have received great attention all over the world. Among all the blueberry harvesting technologies, vision-based blueberry detection and positioning are the main challenges. The key problems of the blueberry picker are the rapid detection and positioning of the blueberry. Compared with other approaches, stereo vision system is a passive sensor, which does not emit light and radiation. And stereo vision system has many advantages such as cheap, accurate, and can acquire rich information.
The thesis aims at the study of blueberry detection and positioning based on the stereo vision system. The basic problem for stereo vision system is the accurate calibration of the cameras. Different from that in the laboratory environment, the calibration for blueberry picker must be done quickly and accurately as the cameras are mounted on the robot, and the work space of the robot is the outside and many should be in the forestry, therefore the calibration procedure should be flexible enough. In the system that we have implemented, a flexible technique has been used to easily calibrate a camera. It is well suited for use without specialized knowledge of computer geometry. The calibration technique only requires the camera to observe some ticked points on the arm. While the arm is moving, a serial of points can be acquired. The test results show that the proposed method is valid.
The stereo matching problem and hand-eye calibration system are discussed in detail in this thesis. And this thesis has done deep research in stereo matching. An inverse stereo matching method is proposed to realize the stereo matching, and the method is better on the effects of the qualitative and quantitative analysis than the traditional template matching method. Through the experimental study of three-dimensional localization and verification, the onboard calibration system and stereotactic hand-eye coordination unity is realized.
According to the demands of the amount of calculation, intelligent level, real time requirement, which asked by the stereo vision system of the blueberry picker, a new blueberry detection method s are proposed based on vision information, including the blueberry segmentation method of the artificial neural network model based on the color information and the method based on the shape information, and they were validated by experiments. A grading method according to the size and shape of blueberry is discussed and experiment based on visual technology and torque description method.
The comprehensive experiments show the blueberries detection, positioning and classification methods based on visual system have high practical and theoretical value.
本论文旨在对蓝莓采摘机器人的基于双目视觉系统的立体视觉检测及定位系统进行研究。立体视觉系统的精确定位是立体视觉系统研究的基础。与实验室环境不同,蓝莓采摘机器人的主要应用背景为林区和野外,所以要求摄像机的定标过程应快速、精确,且必须是在线标定。围绕这个中心,本文提出一种快速精确的定标方案,该方案环境适应性强,只需要在车辆手臂任意位置处标记几个点,让手臂在视觉系统中运动几个位置,即可完成整个在线标定工作。与传统方法相比,本文提出的方法具有操作简单,精度高等优点。
本文对于立体匹配及手眼标定等算法进行了深入研究。立体匹配以提高实时性和精度为目标,利用反向投影方法实现了机器人视觉系统的快速匹配。本文提出的方法在定性、定量分析效果上都优于传统模板匹配方法。通过立体定位的实验研究与验证,实现了将手眼定标系统与立体定位的协调统一。
根据蓝莓采摘机器人对立体视觉系统的计算量、智能化水平、实时性要求,本文提出了基于视觉信息的蓝莓检测方法。包括基于颜色信息结合人工神经元网络模型的蓝莓分割方法和基于形状信息的蓝莓检测方法,并进行了实验验证。阐述了利用视觉技术,采用矩描述方法,根据蓝莓形状及尺寸进行蓝莓分级研究及实验验证。
通过综合实验表明,本文提出的基于视觉系统的蓝莓检测、定位及分级方法具有较高的实用和理论价值。
Blueberry picker is an intelligent robot, which can run autonomously and continuously on outside and hills. Its research, which involves theories and technologies of many aspects, and embodies the latest achievements of information and artificial intelligence, is of great value in research and application. In recent years, all fruit pickers and harvest robots in agriculture and forestry have received great attention all over the world. Among all the blueberry harvesting technologies, vision-based blueberry detection and positioning are the main challenges. The key problems of the blueberry picker are the rapid detection and positioning of the blueberry. Compared with other approaches, stereo vision system is a passive sensor, which does not emit light and radiation. And stereo vision system has many advantages such as cheap, accurate, and can acquire rich information.
The thesis aims at the study of blueberry detection and positioning based on the stereo vision system. The basic problem for stereo vision system is the accurate calibration of the cameras. Different from that in the laboratory environment, the calibration for blueberry picker must be done quickly and accurately as the cameras are mounted on the robot, and the work space of the robot is the outside and many should be in the forestry, therefore the calibration procedure should be flexible enough. In the system that we have implemented, a flexible technique has been used to easily calibrate a camera. It is well suited for use without specialized knowledge of computer geometry. The calibration technique only requires the camera to observe some ticked points on the arm. While the arm is moving, a serial of points can be acquired. The test results show that the proposed method is valid.
The stereo matching problem and hand-eye calibration system are discussed in detail in this thesis. And this thesis has done deep research in stereo matching. An inverse stereo matching method is proposed to realize the stereo matching, and the method is better on the effects of the qualitative and quantitative analysis than the traditional template matching method. Through the experimental study of three-dimensional localization and verification, the onboard calibration system and stereotactic hand-eye coordination unity is realized.
According to the demands of the amount of calculation, intelligent level, real time requirement, which asked by the stereo vision system of the blueberry picker, a new blueberry detection method s are proposed based on vision information, including the blueberry segmentation method of the artificial neural network model based on the color information and the method based on the shape information, and they were validated by experiments. A grading method according to the size and shape of blueberry is discussed and experiment based on visual technology and torque description method.
The comprehensive experiments show the blueberries detection, positioning and classification methods based on visual system have high practical and theoretical value.
引文
[1]Y. Sarig. Robotics of Fruit Harvesting:A State-of-the-art Review[J]. Journal of Agricultural Engineering Research,1993,54(4):265-280
[2]N. Kondo, Y. Nishitsuji, P. Ling, et al. Visual Feedback Guided Robotic Cherry Tomato Harvesting[J]. Transactions of the AS AE,1996,39(6):2331-2338
[3]N. Kondo, K. Ting. Robotics for Bioproduction Systems[M]. American Society of Agricultural Engineers,1998
[4]M. Monta, N. Kondo, Y. Shibano. Agricultural Robot in Grape Production System[C]//Robotics and Automation,1995. Proceedings.,1995 IEEE International Conference on.2002,3:2504-2509
[5]R. Noble, J. Reed, S. Miles, et al. Influence of Mushroom Strains and Population Density on the Performance of a Robotic Harvester [J]. Journal of Agricultural Engineering Research,1997,68(3):215-222
[6]A. Losito. AUTOMATIC Mushroom Trimming Machine,1973. US Patent 3,734,004.
[7]J. Reed, R. Tillett. Initial Experiments in Robotic Mushroom Harvesting [J]. Mechatronics, 1994,4(3):265-279
[8]J. Reed, S. Crook,W. He. Harvesting Mushrooms by Robot[J]. Mushroom Science, 1995:385-391
[9]陈利兵.草莓收获机器人采摘系统研究[D]2005.9北京：中国农业大学
[10]宋健,张铁中,徐丽明,汤修映.果蔬采摘机器人研究进展与展望[J].农业机械学报,2006,37(5)：158-162
[11]刘长林,张铁中,杨丽.果蔬采摘机器人研究进展[J].安徽农业科学,2008,36(13)：5394--5397
[12]赵金英,张铁中,杨丽.西红柿采摘机器人视觉系统的目标提取[J].农业机械学报,2006,37(10)：200-203
[13]S. Hayashi, K. Ganno, Y. Ishii, et al. Robotic Harvesting System for Eggplants [J] Japan Agricultural Research Quarterly,2002,36(3):163-168
[14]N. Kondo, T. Fujiura, K. Ting, et al. Robots in Bioproduction Within Controlled Environments [J]. Robotics for Bioproduction systems,1998:188-226
[15]胡桂仙,于勇.农业机器人的开发与应用[J].中国农机化,2002,(5)：45-47
[16]曹其新,永田雅辉.草莓拣选机器人的开发[J].上海交通大学学报,999,33(7)：880-884
[17]周云山,李强.计算机视觉在蘑菇采摘机器人上的应用[J].农业工程学报,1995,11(4)：27-32
[18]Y. Edan, D. Rogozin, T. Flash, et al. Robotic Melon Harvesting [J]. IEEE Transactions on Robotics and Automation,2002,16(6):831-835
[19]陆怀民.林木球果采集机器人设计与试验[J].农业机械学报,2001,32(6)：52-54
[20]袁国勇.黄瓜采摘机器人目标识别与定位系统的研究[D],2006.北京：中国农业大学
[21]J. Kim, K. Hong. Color-texture Segmentation Using Unsupervised Graph Cuts[J].Pattern Recognition,2009,42(5):735-750
[22]R. Ohlander, K. Price, D. Reddy. Picture Segmentation Using a Recursive Region plitting Method [J]. Computer Graphics and Image Processing,1978,89(3):313-333
[23]F.Kurugollu, B. Sankur, A. Harmanci. Color Image Segmentation Using Histogram Multithresholding and Fusion[J]. Image and vision computing,2001,19(13):915-928
[24]P. Suhasini, K. Krishna, I. Krishna. Graph Based Segmentation in Content Based Image Retrieval[J]. Journal of Computer Science,2008,4(8):699-705.
[25]J. Shi, J. Malik. Normalized Cuts and Image Segmentation[J]. IEEE Transactions on pattern analysis and machine intelligence,2000,22(8):888-905
[26]Y. Boykov, G. Funka-Lea. Graph Cuts and Efficient Nd Image Segmentatio[J]. International Journal of Computer Vision,2006,70(2):109-131
[27]M. Tkalcic, J. Tasic Colour Spaces-perceptual, Historical and Applicational Background[C]//Eurocon.2003:304-308
[28]D. Arthur, S. Vassilvitskii. k-means:The Advantages of Careful Seeding[C]//roceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms.007:1035
[29]D. Comaniciu, P. Meer. Mean Shift:A Robust Approach Toward Feature Space Analysis[J]. IEEE Transactions on pattern analysis and machine intelligence,2002:603-619
[30]L. Vincent, P. Soille. Watersheds in Digital Spaces:An Efficient Algorithm Based on Immersion Simulations[J]. IEEE transactions on pattern analysis and machine intelligence, 1991,13(6):583-598
[31]J. Fan, D. Yau, A. Elmagarmid, et al. Automatic Image Segmentation by Integrating Color-edge Extraction and Seeded Region Growing[J]. IEEE Transactions on Image Processing,2001,10(10):1454-1466
[32]A. Shiozaki. Edge Extraction Using Entropy Operator [J]. Computer Vision, Graphics, and Image Processing,1986,36(1):1-9
[33]B. Chien, M. Cheng. A Color Image Segmentation Approach Based on Fuzzy Similaritymeasure[C]//Fuzzy Systems,2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International CONFERENCE on.2002,1
[34]Z. Dokur. A Unified Framework for Image Compression and Segmentation by Using an Incremental Neural Network[J]. Expert Systems with Applications,2008,34(1):611-619
[35]R. Bajcsy, S. Lee, A. Leonardis. Detection of Diffuse and Specular Interface Reflections and Inter-reflections by Color Image Segmentation[J]. International Journal of Computer vision,1996,17(3):241-272
[36]S. Zhu, A. Yuille. aRegion Competition:Unifying Snakvs, Region Growing, and Bayes /mdl for Multiband Image Segmentation [J]. o IEEE Trans. Pattern Analysis and Machine Intelligence,1996,18(9):884-900
[37]A. Hoogs, R. Collins, R. Kaucic, et al. A Common Set of Perceptual Observables for Grouping, Figure-ground Discrimination, and Texture Classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(4):458-474
[38]L. Chen. Topological Structure in Visual Perception[J]. Science,1982,218(12):699-700.
[39]L. Chen. The Topological Approach to Perceptual Organization[J]. Visual Cognition, 12(4):553-637
[40]E. Borenstein, E. Sharon, S. Ullman. Combining Top-down and Bottom-up Segmentation[C]//Proceedings IEEE workshop on Perceptual Organization in Computer Vision, CVPR 2004.
[41]A. Needham, R. Baillargeon. Effects of Prior Experience on 4.5-month Old Infants Object Segregation[J]. Infant Behavior and Development,1998,21(1):1-24
[42]M. Peterson, B. Gibson. Object Recognition Contributions to Figure-ground Organization:Operations on Outlines and Subjective Contours[J]. Perception and Psychophysics,1994,56(5):551-564
[43]A. Yuille, P. Hallinan, D. Cohen. Feature Extraction from Faces Using Deformable Templates[J]. International journal of computer vision,1992,8(2):99-111
[44]T. Cootes, C. Taylor, D. Cooper, et al. Active Shape Models-their Training and Application[J]. Computer vision and image understanding,1995,61(1):38-59
[45]M. Kass, A. Witkin, D. Terzopoulos. Snakes:Active Contour Models[J]. International journal of computer vision,1988,1(4):321-331
[46]E. Borenstein, S. Ullman. Class-specific, Top-down Segmentation[J]. Lecture notes in computer science,2002:109-124
[47]K. W. Lee, H. Buxton, J. Feng. Cui-Guided Search:A Computational Model of Selective Attention[J]. IEEE Transactions on Neural Networks,2005,16(4):103-109
[48]J. Malik, S. Belongie, J. Shi, et al. Textons, Contours and Regions:Cue Integration in Image Segmentation[C]//Proceedings of the International CONFERENCE on Computer Vision.1999,2:918
[49]J. Malik, S. Belongie, T. Leung, et al. Contour and Texture Analysis for Image Segmentation[J]. International Journal of Computer Vision,2001,43(1):7-27
[50]Z. Tu, X. Chen, A. Yuille, et al. Image Parsing:Unifying Segmentation, Detection, and Recognition[J]. International Journal of Computer Vision,2005,63(2):113-140
[51]Z. Tu, S. Zhu. Image Segmentation by Data-driven Markov Chain Monte Carlo[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002:657-673
[52]魏国庆,马颂德.3D Motion Estimation and Motion Fusion by Affine Region Matching[J].计算机科学与技术学报：英文版,1993,8(1)：17-25
[53]A. Fitzgibbon, M. Pilu, R. Fisher. Direct Least Square Fitting of Ellipses[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,21(5):476-480
[54]P. Yin. A New Circle/ellipse Detector Using Genetic Algorithms[J]. Pattern Recognition Letters,1999,20(7):731-740
[55]R. Krishnapuram, O. Nasraoui, H. Frigui. The Fuzzy C Spherical Shells Algorithm:A New Approach[J]. Neural Networks, IEEE Transactions on,2002,3(5):663-671
[56]R. Dave. Generalized Fuzzy C-shells Clustering and Detection of Circular and Elliptical Boundaries[J]. Pattern Recognition,1992,25(7):713-721
[57]N. Guil, E. Zapata. Lower Order Circle and Ellipse Hough Transform* 1[J]. Pattern Recognition,1997,30(10):1729-1744
[58]R. Duda, P. Hart. Use of the Hough Transformation to Detect Lines and Curves in Pictures[J]. Communications of the ACM,1972,15(1):11-15
[59]J. Illingworth, J. Kittler. A Survey of the Hough Transform[J]. Computer vision,graphics, and image processing,1988,44(1):87-116
[60]A. Goneid, S. El-Gindi, A. Sewisy. A Method for the Hough Transform Detection of Circles and Ellipses Using a 1-dimensional Array[C]//Systems, Man, and Cybernetics, 1997.'Computational Cybernetics and Simulation'.,1997 IEEE International CONFERENCE on.2002,4:3154-3157
[61]Y. Lei, K. Wong. Ellipse Detection Based on Symmetry[J]. Pattern recognition letters, 1999,20(1):41-47
[62]P. Nair, A. Saunders, et al. Hough Transform Based Ellipse Detection Algorithm[J]. Pattern Recognition Letters,1996,17(7):777-784
[63]G. Foresti, C. Regazzoni, G. Vernazza. Circular Arc Extraction by Direct Clustering [in a 3d Hough Parameter Space[J]. Signal Processing,1995,41(2):203-224
[64]S. Guo, X. Zhang, F. Zhang. Adaptive Randomized Hough Transform for Circle Detection Using Moving Window[C]//Machine Learning and Cybernetics,2006 International CONFERENCE on.2006:3880-3885
[65]W. Yi. Circle Detection Using Improved Dynamic Generalized Hough Transform [C] Geoscience and Remote Sensing Symposium Proceedings,1998.IGARSS'98.1998 IEEE International CONFERENCE on.2002,2:1190-1192
[66]L. Wu, X. Liu, L. Yin. Object Recognition in Complex Backgrounds[C]//Signal Processing,2004. Proceedings. ICSP'04.20047th International CONFERENCE.2005, 2:1601-1604
[67]L. Pan, M. Xie. Research on Iris Image Preprocessing Algorithm[C]//Communications and Information Technology,2005. ISCIT 2005.IEEE International Symposium on.2006, 1:161-164
[68]B. Araabi, N. Kehtarnavaz. Hough Array Processing via Fast Multi-scale Clustering[J]. Real-Time Imaging,2000,6(2):129-141
[69]J. Han, L. K'oczy, T. Poston. Fuzzy Hough Transform[J]. Pattern Recognition Letters, 1994,15(7):649-658
[70]C. Chau,W. Siu. Adaptive Dual-point Hough Transform for Object Recognition[J]. Computer Vision and Image Understanding,2004,96(1):1-16
[71]A. Kassim, Z. Mian, M. Mannan. Connectivity Oriented Fast Hough Transform for Tool Wear Monitoring [J]. Pattern Recognition,2004,37(9):1925-1933
[72]C. Olson. Constrained Hough Transforms for Curve Detection [J]. Computer Vision and Image Understanding,1999,73(3):329-345
[73]J. Canny. A Computational Approach to Edge Detection[J]. Readings in computer vision: issues, problems, principles, and paradigms,1987,184
[74]S. Chiu, J. Liaw. An Effective Voting Method for Circle Detection[J]. Pattern Recognition Letters,2005,26(2):121-133
[75]M. Hu. Visual Pattern Recognition by Moment Invariants[J]. Information Theory,IRE Transactions on,1962,8(2):179-187
[76]J. Shi, J. Malik. Normalized Cuts and Image Segmentation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,22(8):888-905
[77]S. Zhu, A. Yuille. Region Competition:Unifying Snakes, Region Growing and Bayes/mdl for Multiband Image Segmentation [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,18(9):884-900
[78]F. Crick. The Astonishing Hypothesis[M]. New York,1994
[79]E. Kandel, J. Schwartz, T. Jessell. Essentials of Neural Science and Behavior[M]. McGraw-Hill Professional,1995
[80]J. Zacks, J. Mires, B. Tversky, et al. Mental Spatial Transformations of Objects and Perspective[J]. Spatial Cognition and Computation,2000,2(4):315-332
[81]S. Edelman. Constraining the Neural Representation of the VisualWorld[J]. Trends in Cognitive Sciences,2002,6(3):125-131
[82]R. Shiffrin. Modeling Memory and Perception [J]. Cognitive Science,2003,27(3):341-378
[83]M. Xie. Robot Vision:A Holistic View[C]//Climbing and walking robots:proceedings of the 7th International CONFERENCE CLAWAR 2004
[84]L. Roberts, et al. Machine Perception of Three-dimensional Solids[M]. Garland Pub., 1980
[85]D. Marr. Vision:A Computational Investigation Into the Human Representation and Processing of Visual Information[M]. Henry Holt and Co., Inc. New York, NY, USA, 1982
[86]D. Lowe. Three-dimensional Object Recognition from Single Two-dimensional Images[J]. Artificial intelligence,1987,31(3):355-395
[87]I. Biederman. Recognition-by-components:A Theory of Human Image Understanding[J]. Psychological review,1987,94(2):115-147
[88]I. Biederman. Visual Object Recognition[J]. An Invitation to Cognitive Science:Visual cognition/edited by Stephen M. Kosslyn and Daniel N. Osherson,1995,121-200
[89]I. Bihdlkman. Recent Psychophysical and Neural Research in Shape Recognition [J]. Psychobiology,26(4):309-331
[90]A. Treisman, G. Gelade. A Feature-integration Theory of Attention[J]. Cognitive psychology,1980,12(1):97-136
[91]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision[M]. Cambridge Univ Pr,2003
[92]R. Bracewell. The Fourier Transform[J]. Scientific American,1989,260(6):86-95
[93]E. Brigham. The Fast Fourier Transform and its Applications[M]. Prentice Hall Englewood Cliffs, NJ,1988
[94]L. Grafakos. Classical and Modern Fourier Analysis[M]. Prentice Hall,2004.
[95]C. Chui. An Introduction to Wavelets[M]. Academic Pr,1992
[96]C. Chui, L. Montefusco, L. Puccio. Wavelets:Theory, Algorithms, and Applications[M]. Academic Pr,1994
[97]R. Bailey, M. Srinath. Orthogonal Moment Features for Use with Parametric Andnon-parametric Classifiers [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(4):389-399
[98]J. Boyce, W. Hossack. Moment Invariants for Pattern Recognition [J]. Pattern Recognition Letters,1983,1(5-6):451-456
[99]A. Loui, A. Venetsanopoulos, K. Smith. Two-dimensional Shape Representation Using Morphologicalcorrelation Functions [C]//Acoustics, Speech, and Signal Processing,1990. ICASSP-90,1990 International CONFERENCE on.1990:2165-2168
[100]I. Jolliffe. Principal Component Analysis[M]. Springer verlag,2002
[101]肖茜鲁,宏伟.基于高斯平滑的自适应角点检测.计算机辅助设计与图形学学.2003.15(11)：1358-1361
[102]钟宝江,廖文和.基于精化曲线累加弦长的角点检测技术.计算机辅助设计与图形学学报.2004,16(7)：939-943
[103]刘忠,梁晓庚,贾晓洪,曹秉刚.基于四元数的导弹全方位姿态运动误差研究.弹道学报.Vol.2006,18(2)：5-8
[104]杨杨,张田文.角点检测算法评价方法的研究.哈尔滨工业大学学报.1998,30(2)：7-10
[105]付京逊等.机器人学.中国科学技术出版社.1989：9-30
[106]郭焱等.虚拟植物的研究进展.科学通报,2001(4-46)：273-280
[107]郭焱等.玉米冠层三维结构研究.作物学报,1998(6-24)：1006-1009
[108]胡包钢等.植物生长建模与可视化—回顾与展望,自动化学报,Vol.2,2002
[109]杨京平,王兆套.作物生长模拟模型及其应用.应用生态学报,1999,10(4)：501-506
[110]赵星.忠实于植物学的虚拟植物生长研究.博士学位论文.中国科技大学,2001
[111]赵星等.虚拟植物生长的双尺度自动机模型.计算机学报.2001(6-24)：524-529
[112]伍怡.三维植物器官的几何建模.硕士学位论文.中国科学院自动化所,2001
[113]严红平.基于子结构和水力模型的虚拟植物建模和可视化研究,博士学位论文,中科院自动化所,2002
[2]N. Kondo, Y. Nishitsuji, P. Ling, et al. Visual Feedback Guided Robotic Cherry Tomato Harvesting[J]. Transactions of the AS AE,1996,39(6):2331-2338
[3]N. Kondo, K. Ting. Robotics for Bioproduction Systems[M]. American Society of Agricultural Engineers,1998
[4]M. Monta, N. Kondo, Y. Shibano. Agricultural Robot in Grape Production System[C]//Robotics and Automation,1995. Proceedings.,1995 IEEE International Conference on.2002,3:2504-2509
[5]R. Noble, J. Reed, S. Miles, et al. Influence of Mushroom Strains and Population Density on the Performance of a Robotic Harvester [J]. Journal of Agricultural Engineering Research,1997,68(3):215-222
[6]A. Losito. AUTOMATIC Mushroom Trimming Machine,1973. US Patent 3,734,004.
[7]J. Reed, R. Tillett. Initial Experiments in Robotic Mushroom Harvesting [J]. Mechatronics, 1994,4(3):265-279
[8]J. Reed, S. Crook,W. He. Harvesting Mushrooms by Robot[J]. Mushroom Science, 1995:385-391
[9]陈利兵.草莓收获机器人采摘系统研究[D]2005.9北京：中国农业大学
[10]宋健,张铁中,徐丽明,汤修映.果蔬采摘机器人研究进展与展望[J].农业机械学报,2006,37(5)：158-162
[11]刘长林,张铁中,杨丽.果蔬采摘机器人研究进展[J].安徽农业科学,2008,36(13)：5394--5397
[12]赵金英,张铁中,杨丽.西红柿采摘机器人视觉系统的目标提取[J].农业机械学报,2006,37(10)：200-203
[13]S. Hayashi, K. Ganno, Y. Ishii, et al. Robotic Harvesting System for Eggplants [J] Japan Agricultural Research Quarterly,2002,36(3):163-168
[14]N. Kondo, T. Fujiura, K. Ting, et al. Robots in Bioproduction Within Controlled Environments [J]. Robotics for Bioproduction systems,1998:188-226
[15]胡桂仙,于勇.农业机器人的开发与应用[J].中国农机化,2002,(5)：45-47
[16]曹其新,永田雅辉.草莓拣选机器人的开发[J].上海交通大学学报,999,33(7)：880-884
[17]周云山,李强.计算机视觉在蘑菇采摘机器人上的应用[J].农业工程学报,1995,11(4)：27-32
[18]Y. Edan, D. Rogozin, T. Flash, et al. Robotic Melon Harvesting [J]. IEEE Transactions on Robotics and Automation,2002,16(6):831-835
[19]陆怀民.林木球果采集机器人设计与试验[J].农业机械学报,2001,32(6)：52-54
[20]袁国勇.黄瓜采摘机器人目标识别与定位系统的研究[D],2006.北京：中国农业大学
[21]J. Kim, K. Hong. Color-texture Segmentation Using Unsupervised Graph Cuts[J].Pattern Recognition,2009,42(5):735-750
[22]R. Ohlander, K. Price, D. Reddy. Picture Segmentation Using a Recursive Region plitting Method [J]. Computer Graphics and Image Processing,1978,89(3):313-333
[23]F.Kurugollu, B. Sankur, A. Harmanci. Color Image Segmentation Using Histogram Multithresholding and Fusion[J]. Image and vision computing,2001,19(13):915-928
[24]P. Suhasini, K. Krishna, I. Krishna. Graph Based Segmentation in Content Based Image Retrieval[J]. Journal of Computer Science,2008,4(8):699-705.
[25]J. Shi, J. Malik. Normalized Cuts and Image Segmentation[J]. IEEE Transactions on pattern analysis and machine intelligence,2000,22(8):888-905
[26]Y. Boykov, G. Funka-Lea. Graph Cuts and Efficient Nd Image Segmentatio[J]. International Journal of Computer Vision,2006,70(2):109-131
[27]M. Tkalcic, J. Tasic Colour Spaces-perceptual, Historical and Applicational Background[C]//Eurocon.2003:304-308
[28]D. Arthur, S. Vassilvitskii. k-means:The Advantages of Careful Seeding[C]//roceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms.007:1035
[29]D. Comaniciu, P. Meer. Mean Shift:A Robust Approach Toward Feature Space Analysis[J]. IEEE Transactions on pattern analysis and machine intelligence,2002:603-619
[30]L. Vincent, P. Soille. Watersheds in Digital Spaces:An Efficient Algorithm Based on Immersion Simulations[J]. IEEE transactions on pattern analysis and machine intelligence, 1991,13(6):583-598
[31]J. Fan, D. Yau, A. Elmagarmid, et al. Automatic Image Segmentation by Integrating Color-edge Extraction and Seeded Region Growing[J]. IEEE Transactions on Image Processing,2001,10(10):1454-1466
[32]A. Shiozaki. Edge Extraction Using Entropy Operator [J]. Computer Vision, Graphics, and Image Processing,1986,36(1):1-9
[33]B. Chien, M. Cheng. A Color Image Segmentation Approach Based on Fuzzy Similaritymeasure[C]//Fuzzy Systems,2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International CONFERENCE on.2002,1
[34]Z. Dokur. A Unified Framework for Image Compression and Segmentation by Using an Incremental Neural Network[J]. Expert Systems with Applications,2008,34(1):611-619
[35]R. Bajcsy, S. Lee, A. Leonardis. Detection of Diffuse and Specular Interface Reflections and Inter-reflections by Color Image Segmentation[J]. International Journal of Computer vision,1996,17(3):241-272
[36]S. Zhu, A. Yuille. aRegion Competition:Unifying Snakvs, Region Growing, and Bayes /mdl for Multiband Image Segmentation [J]. o IEEE Trans. Pattern Analysis and Machine Intelligence,1996,18(9):884-900
[37]A. Hoogs, R. Collins, R. Kaucic, et al. A Common Set of Perceptual Observables for Grouping, Figure-ground Discrimination, and Texture Classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(4):458-474
[38]L. Chen. Topological Structure in Visual Perception[J]. Science,1982,218(12):699-700.
[39]L. Chen. The Topological Approach to Perceptual Organization[J]. Visual Cognition, 12(4):553-637
[40]E. Borenstein, E. Sharon, S. Ullman. Combining Top-down and Bottom-up Segmentation[C]//Proceedings IEEE workshop on Perceptual Organization in Computer Vision, CVPR 2004.
[41]A. Needham, R. Baillargeon. Effects of Prior Experience on 4.5-month Old Infants Object Segregation[J]. Infant Behavior and Development,1998,21(1):1-24
[42]M. Peterson, B. Gibson. Object Recognition Contributions to Figure-ground Organization:Operations on Outlines and Subjective Contours[J]. Perception and Psychophysics,1994,56(5):551-564
[43]A. Yuille, P. Hallinan, D. Cohen. Feature Extraction from Faces Using Deformable Templates[J]. International journal of computer vision,1992,8(2):99-111
[44]T. Cootes, C. Taylor, D. Cooper, et al. Active Shape Models-their Training and Application[J]. Computer vision and image understanding,1995,61(1):38-59
[45]M. Kass, A. Witkin, D. Terzopoulos. Snakes:Active Contour Models[J]. International journal of computer vision,1988,1(4):321-331
[46]E. Borenstein, S. Ullman. Class-specific, Top-down Segmentation[J]. Lecture notes in computer science,2002:109-124
[47]K. W. Lee, H. Buxton, J. Feng. Cui-Guided Search:A Computational Model of Selective Attention[J]. IEEE Transactions on Neural Networks,2005,16(4):103-109
[48]J. Malik, S. Belongie, J. Shi, et al. Textons, Contours and Regions:Cue Integration in Image Segmentation[C]//Proceedings of the International CONFERENCE on Computer Vision.1999,2:918
[49]J. Malik, S. Belongie, T. Leung, et al. Contour and Texture Analysis for Image Segmentation[J]. International Journal of Computer Vision,2001,43(1):7-27
[50]Z. Tu, X. Chen, A. Yuille, et al. Image Parsing:Unifying Segmentation, Detection, and Recognition[J]. International Journal of Computer Vision,2005,63(2):113-140
[51]Z. Tu, S. Zhu. Image Segmentation by Data-driven Markov Chain Monte Carlo[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002:657-673
[52]魏国庆,马颂德.3D Motion Estimation and Motion Fusion by Affine Region Matching[J].计算机科学与技术学报：英文版,1993,8(1)：17-25
[53]A. Fitzgibbon, M. Pilu, R. Fisher. Direct Least Square Fitting of Ellipses[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,21(5):476-480
[54]P. Yin. A New Circle/ellipse Detector Using Genetic Algorithms[J]. Pattern Recognition Letters,1999,20(7):731-740
[55]R. Krishnapuram, O. Nasraoui, H. Frigui. The Fuzzy C Spherical Shells Algorithm:A New Approach[J]. Neural Networks, IEEE Transactions on,2002,3(5):663-671
[56]R. Dave. Generalized Fuzzy C-shells Clustering and Detection of Circular and Elliptical Boundaries[J]. Pattern Recognition,1992,25(7):713-721
[57]N. Guil, E. Zapata. Lower Order Circle and Ellipse Hough Transform* 1[J]. Pattern Recognition,1997,30(10):1729-1744
[58]R. Duda, P. Hart. Use of the Hough Transformation to Detect Lines and Curves in Pictures[J]. Communications of the ACM,1972,15(1):11-15
[59]J. Illingworth, J. Kittler. A Survey of the Hough Transform[J]. Computer vision,graphics, and image processing,1988,44(1):87-116
[60]A. Goneid, S. El-Gindi, A. Sewisy. A Method for the Hough Transform Detection of Circles and Ellipses Using a 1-dimensional Array[C]//Systems, Man, and Cybernetics, 1997.'Computational Cybernetics and Simulation'.,1997 IEEE International CONFERENCE on.2002,4:3154-3157
[61]Y. Lei, K. Wong. Ellipse Detection Based on Symmetry[J]. Pattern recognition letters, 1999,20(1):41-47
[62]P. Nair, A. Saunders, et al. Hough Transform Based Ellipse Detection Algorithm[J]. Pattern Recognition Letters,1996,17(7):777-784
[63]G. Foresti, C. Regazzoni, G. Vernazza. Circular Arc Extraction by Direct Clustering [in a 3d Hough Parameter Space[J]. Signal Processing,1995,41(2):203-224
[64]S. Guo, X. Zhang, F. Zhang. Adaptive Randomized Hough Transform for Circle Detection Using Moving Window[C]//Machine Learning and Cybernetics,2006 International CONFERENCE on.2006:3880-3885
[65]W. Yi. Circle Detection Using Improved Dynamic Generalized Hough Transform [C] Geoscience and Remote Sensing Symposium Proceedings,1998.IGARSS'98.1998 IEEE International CONFERENCE on.2002,2:1190-1192
[66]L. Wu, X. Liu, L. Yin. Object Recognition in Complex Backgrounds[C]//Signal Processing,2004. Proceedings. ICSP'04.20047th International CONFERENCE.2005, 2:1601-1604
[67]L. Pan, M. Xie. Research on Iris Image Preprocessing Algorithm[C]//Communications and Information Technology,2005. ISCIT 2005.IEEE International Symposium on.2006, 1:161-164
[68]B. Araabi, N. Kehtarnavaz. Hough Array Processing via Fast Multi-scale Clustering[J]. Real-Time Imaging,2000,6(2):129-141
[69]J. Han, L. K'oczy, T. Poston. Fuzzy Hough Transform[J]. Pattern Recognition Letters, 1994,15(7):649-658
[70]C. Chau,W. Siu. Adaptive Dual-point Hough Transform for Object Recognition[J]. Computer Vision and Image Understanding,2004,96(1):1-16
[71]A. Kassim, Z. Mian, M. Mannan. Connectivity Oriented Fast Hough Transform for Tool Wear Monitoring [J]. Pattern Recognition,2004,37(9):1925-1933
[72]C. Olson. Constrained Hough Transforms for Curve Detection [J]. Computer Vision and Image Understanding,1999,73(3):329-345
[73]J. Canny. A Computational Approach to Edge Detection[J]. Readings in computer vision: issues, problems, principles, and paradigms,1987,184
[74]S. Chiu, J. Liaw. An Effective Voting Method for Circle Detection[J]. Pattern Recognition Letters,2005,26(2):121-133
[75]M. Hu. Visual Pattern Recognition by Moment Invariants[J]. Information Theory,IRE Transactions on,1962,8(2):179-187
[76]J. Shi, J. Malik. Normalized Cuts and Image Segmentation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,22(8):888-905
[77]S. Zhu, A. Yuille. Region Competition:Unifying Snakes, Region Growing and Bayes/mdl for Multiband Image Segmentation [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2002,18(9):884-900
[78]F. Crick. The Astonishing Hypothesis[M]. New York,1994
[79]E. Kandel, J. Schwartz, T. Jessell. Essentials of Neural Science and Behavior[M]. McGraw-Hill Professional,1995
[80]J. Zacks, J. Mires, B. Tversky, et al. Mental Spatial Transformations of Objects and Perspective[J]. Spatial Cognition and Computation,2000,2(4):315-332
[81]S. Edelman. Constraining the Neural Representation of the VisualWorld[J]. Trends in Cognitive Sciences,2002,6(3):125-131
[82]R. Shiffrin. Modeling Memory and Perception [J]. Cognitive Science,2003,27(3):341-378
[83]M. Xie. Robot Vision:A Holistic View[C]//Climbing and walking robots:proceedings of the 7th International CONFERENCE CLAWAR 2004
[84]L. Roberts, et al. Machine Perception of Three-dimensional Solids[M]. Garland Pub., 1980
[85]D. Marr. Vision:A Computational Investigation Into the Human Representation and Processing of Visual Information[M]. Henry Holt and Co., Inc. New York, NY, USA, 1982
[86]D. Lowe. Three-dimensional Object Recognition from Single Two-dimensional Images[J]. Artificial intelligence,1987,31(3):355-395
[87]I. Biederman. Recognition-by-components:A Theory of Human Image Understanding[J]. Psychological review,1987,94(2):115-147
[88]I. Biederman. Visual Object Recognition[J]. An Invitation to Cognitive Science:Visual cognition/edited by Stephen M. Kosslyn and Daniel N. Osherson,1995,121-200
[89]I. Bihdlkman. Recent Psychophysical and Neural Research in Shape Recognition [J]. Psychobiology,26(4):309-331
[90]A. Treisman, G. Gelade. A Feature-integration Theory of Attention[J]. Cognitive psychology,1980,12(1):97-136
[91]R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision[M]. Cambridge Univ Pr,2003
[92]R. Bracewell. The Fourier Transform[J]. Scientific American,1989,260(6):86-95
[93]E. Brigham. The Fast Fourier Transform and its Applications[M]. Prentice Hall Englewood Cliffs, NJ,1988
[94]L. Grafakos. Classical and Modern Fourier Analysis[M]. Prentice Hall,2004.
[95]C. Chui. An Introduction to Wavelets[M]. Academic Pr,1992
[96]C. Chui, L. Montefusco, L. Puccio. Wavelets:Theory, Algorithms, and Applications[M]. Academic Pr,1994
[97]R. Bailey, M. Srinath. Orthogonal Moment Features for Use with Parametric Andnon-parametric Classifiers [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(4):389-399
[98]J. Boyce, W. Hossack. Moment Invariants for Pattern Recognition [J]. Pattern Recognition Letters,1983,1(5-6):451-456
[99]A. Loui, A. Venetsanopoulos, K. Smith. Two-dimensional Shape Representation Using Morphologicalcorrelation Functions [C]//Acoustics, Speech, and Signal Processing,1990. ICASSP-90,1990 International CONFERENCE on.1990:2165-2168
[100]I. Jolliffe. Principal Component Analysis[M]. Springer verlag,2002
[101]肖茜鲁,宏伟.基于高斯平滑的自适应角点检测.计算机辅助设计与图形学学.2003.15(11)：1358-1361
[102]钟宝江,廖文和.基于精化曲线累加弦长的角点检测技术.计算机辅助设计与图形学学报.2004,16(7)：939-943
[103]刘忠,梁晓庚,贾晓洪,曹秉刚.基于四元数的导弹全方位姿态运动误差研究.弹道学报.Vol.2006,18(2)：5-8
[104]杨杨,张田文.角点检测算法评价方法的研究.哈尔滨工业大学学报.1998,30(2)：7-10
[105]付京逊等.机器人学.中国科学技术出版社.1989：9-30
[106]郭焱等.虚拟植物的研究进展.科学通报,2001(4-46)：273-280
[107]郭焱等.玉米冠层三维结构研究.作物学报,1998(6-24)：1006-1009
[108]胡包钢等.植物生长建模与可视化—回顾与展望,自动化学报,Vol.2,2002
[109]杨京平,王兆套.作物生长模拟模型及其应用.应用生态学报,1999,10(4)：501-506
[110]赵星.忠实于植物学的虚拟植物生长研究.博士学位论文.中国科技大学,2001
[111]赵星等.虚拟植物生长的双尺度自动机模型.计算机学报.2001(6-24)：524-529
[112]伍怡.三维植物器官的几何建模.硕士学位论文.中国科学院自动化所,2001
[113]严红平.基于子结构和水力模型的虚拟植物建模和可视化研究,博士学位论文,中科院自动化所,2002