农业机器人视觉导航系统及其光照问题的研究
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摘要
农业机器人及其视觉导航系统的研究得到了越来越多的关注,成为探索在农业机械装备中应用智能控制等高新技术的重要研究方向。农业机器人工作在复杂的开放式非结构的农田环境,如何从中提取出导航信息是视觉导航的难点。光照变化是使导航信息提取算法变得不稳定的主要因素,阴影的存在更是严重干扰了图像的处理与分析。因此,必须探索去除光照影响的算法,提高导航系统的鲁棒性。人的视觉系统具有颜色恒常性,如何将人的这种颜色恒常性引入到机器视觉中,是解决机器视觉中光照问题的一种思路。
本文以实现农业机器人田间自主导航为目的,首次将颜色恒常性理论用于解决机器视觉导航中的光照问题,并自行设计了农业机器人移动平台,构建了单目视觉导航系统,进行了作物行跟踪和地头转向试验。主要研究成果及结论包括：
(1)从图像的物理成象过程微观地探讨光照对图像的影响,引进了颜色恒常性理论从根本上解决光照问题,并把此方法总结为两种思路：一种是转化到标准光照下的图像,另一种是获得光照不变特征量。
(2)研究测试了几种将非标准光照下的图像校正到标准光照下的图像的颜色恒常性算法。这些算法根据图像的统计信息,全局地或局部地估计出光照色彩,利用对角模型进行颜色校正,从而获得标准图像。提出了基于色彩学习的彩色图像分割方法,通过对原始图像和各种算法获取的标准图像的分割结果进行统计发现,光照变化影响图像的分割质量,而转换到标准光照下的标准图像的分割效果有明显提高。最终,根据算法耗时统计和分割结果统计两项指标进行算法比较认为,灰度世界假设和三种局部光照估计算法分割效果相当,但灰度世界假设在效率上更高,比较适合于实时导航的应用要求。因此,选取灰度世界假设算法来获取标准图像,以解决农业机器人视觉导航中的光照变化问题。
(3)分析了阴影形成的实质,阐述了光照无关图的原理,在此基础上导出了一种基于光照无关图的阴影去除方法。该方法根据光学成像原理通过对图像进行变换来得到一幅与光照无关的灰度图,以达到去除阴影的目的。同时针对该方法需要事先测定摄像机的光照无关角的不便之处,引入了基于最小熵的摄像机光照无关角求取方法。实验表明,采用基于最小熵的光照无关角求取方法得到的光照无关角相差不大,完全可用于获取去除阴影后的光照无关图。针对光照无关图的特点,采用增强最大类间方差法自动求取阈值并进行分割处理,获得了理想的效果。
(4)在建立农田导航作业环境模型的基础上,进行了沿作物行行走作业的导航规划,分析了导航信息提取的主要步骤,提出了优化的霍夫变换用于提取单作物行的导航路径,提出了结合最小二乘的线扫描算法提取双作物行的导航路径,提出了水平投影算法用于地头的检测判断。通过对视觉处理算法测试分析,两种导航路径提取算法和基于水平投影的地头检测算法设计合理可靠,整个视觉导航信息提取算法的总花费时间完全可满足视觉导航的实时性要求。
(5)引入基于CAN总线的分布式控制思想,自行设计了四轮驱动、四轮转向的农业机器人移动平台,构建了单目视觉导航系统,并开发了基于多线程的视觉导航系统软件。在农业机器人移动平台上验证了视觉处理算法的正确性和可靠性,通过作物行跟踪试验,验证了整个视觉导航系统的可行性和可靠性。最后,提出了基于光电编码器的航位推测算法以实现地头转向功能,模拟测试结果表明,基于光电编码器的航位推测算法可满足农业机器人地头转向的工作要求。
Researches on agricultural robot and their vision navigation systems are attracting more and more attentions and becoming an important direction of exploring application of high technology (intelligent control) in agricultural machines and equipments. Agricultural environment is complicated, open and unstructured. How to extract visual information of navigation is a difficulty of vision navigation. Illumination variation makes extracting algorithms of guidance parameters unstable and existence of shadows furthermore interferences image processing and analysis. So some new algorithms of removing illumination influence should be explored to improve stability of vision navigation system. Human visual system has property of color constancy. How to introduce human color constancy into machine vision is a train of thought of solving illumination problem.
In the thesis, to realize autonomous navigation of agricultural robot in the field, illumination influence problem in vision navigation system was studied firstly with color constancy theories, a moblie robot platform for agriculture was designed, vision navigation system was constructed, and trials of crop line guidance and headland turn were carried through. Main research contents and conclusions include:
(1) Light microscopic influence on images was investigated through image fomation process. Theory of color constancy was introduced to solve illumination influence problem, and its current status was reviewed, two color constancy ways of solving illumination influence problem were concluded. One way is to take the conversion of images from in the non-canonical illumination to in the canonical illumination. Another way is to obtain illumination invariants.
(2) Some color constancy algorithms, which to take the conversion of images from in the non-canonical illumination to canonical illumination, were tested. These algorithms estimate illumination color globally and locally according to statistical information of image data, and then obtain color-corrected canonical images by von Kries model. A color learn based color image processing algorithm was proposed, Statistical results of image segmentation experiments to original images and canonical images showed, illumination variation influenced image segmentation quality, but canonical images under canonical illumination could improve segmentation results. Finally, according to time cost and segmentation effects, gray world algorithm and three local illumination estimation algorithm is equivalent in segmentation results, but gray world algorithm is high in efficiency and fit for real-time vision navigation, so gray world algorithm to obtain canonical image is chosen in later vision navigation system.
(3) Shadow formation process was analyzed. Theory of illumination invariant image was described. Based on this, a method of shadow removal was derived. To obtain the illumination invariant image, a camera-dependent illumination invariant angle should be measured. Aiming at solving this problem inconvenient, a method of minimal entropy was introduced. Results showed that method of shadow removal based on illumination invariant image could remove shadows efficiently. An enhanced OSTU threshold method was proposed to segment the illumination invariant image, and perfect effects were obtained.
(4) Based on founded model of farmland navigation working environment, navigation process of agricultural robot in the field was planed and main steps of navigation parameters extraction were analysised. An optimized Hough transform algorithm was proposed to extract guidance parameters for only single crop row. A line-scan algorithm with least squares theory was proposed to extract guidance parameters for double crop rows. A horizontal projection algorithm was used to detect headland. Tests of these image processing algorithms showed, all the algorithms were reasonable and reliable and total time cost of vision algorithms was enough for vision navigation.
(5) A CAN-based distributed control technology was introduced. A 4WD and 4WS robot platform was designed. A monocular vision navigation system was constructed. Multi-thread based vision navigation system software was developed. Validity and reliability of former vision algorithms were verified in the mobile robot platform. Results from cropline tracking tests showed that outdoor vision navigation system was feasible. In the end, an optical-encoder-based Dead Reckoning algorithm was proposed to fulfill headland turn function, and simulated tests showed the algorithm could suffice for headland turn.
本文以实现农业机器人田间自主导航为目的,首次将颜色恒常性理论用于解决机器视觉导航中的光照问题,并自行设计了农业机器人移动平台,构建了单目视觉导航系统,进行了作物行跟踪和地头转向试验。主要研究成果及结论包括：
(1)从图像的物理成象过程微观地探讨光照对图像的影响,引进了颜色恒常性理论从根本上解决光照问题,并把此方法总结为两种思路：一种是转化到标准光照下的图像,另一种是获得光照不变特征量。
(2)研究测试了几种将非标准光照下的图像校正到标准光照下的图像的颜色恒常性算法。这些算法根据图像的统计信息,全局地或局部地估计出光照色彩,利用对角模型进行颜色校正,从而获得标准图像。提出了基于色彩学习的彩色图像分割方法,通过对原始图像和各种算法获取的标准图像的分割结果进行统计发现,光照变化影响图像的分割质量,而转换到标准光照下的标准图像的分割效果有明显提高。最终,根据算法耗时统计和分割结果统计两项指标进行算法比较认为,灰度世界假设和三种局部光照估计算法分割效果相当,但灰度世界假设在效率上更高,比较适合于实时导航的应用要求。因此,选取灰度世界假设算法来获取标准图像,以解决农业机器人视觉导航中的光照变化问题。
(3)分析了阴影形成的实质,阐述了光照无关图的原理,在此基础上导出了一种基于光照无关图的阴影去除方法。该方法根据光学成像原理通过对图像进行变换来得到一幅与光照无关的灰度图,以达到去除阴影的目的。同时针对该方法需要事先测定摄像机的光照无关角的不便之处,引入了基于最小熵的摄像机光照无关角求取方法。实验表明,采用基于最小熵的光照无关角求取方法得到的光照无关角相差不大,完全可用于获取去除阴影后的光照无关图。针对光照无关图的特点,采用增强最大类间方差法自动求取阈值并进行分割处理,获得了理想的效果。
(4)在建立农田导航作业环境模型的基础上,进行了沿作物行行走作业的导航规划,分析了导航信息提取的主要步骤,提出了优化的霍夫变换用于提取单作物行的导航路径,提出了结合最小二乘的线扫描算法提取双作物行的导航路径,提出了水平投影算法用于地头的检测判断。通过对视觉处理算法测试分析,两种导航路径提取算法和基于水平投影的地头检测算法设计合理可靠,整个视觉导航信息提取算法的总花费时间完全可满足视觉导航的实时性要求。
(5)引入基于CAN总线的分布式控制思想,自行设计了四轮驱动、四轮转向的农业机器人移动平台,构建了单目视觉导航系统,并开发了基于多线程的视觉导航系统软件。在农业机器人移动平台上验证了视觉处理算法的正确性和可靠性,通过作物行跟踪试验,验证了整个视觉导航系统的可行性和可靠性。最后,提出了基于光电编码器的航位推测算法以实现地头转向功能,模拟测试结果表明,基于光电编码器的航位推测算法可满足农业机器人地头转向的工作要求。
Researches on agricultural robot and their vision navigation systems are attracting more and more attentions and becoming an important direction of exploring application of high technology (intelligent control) in agricultural machines and equipments. Agricultural environment is complicated, open and unstructured. How to extract visual information of navigation is a difficulty of vision navigation. Illumination variation makes extracting algorithms of guidance parameters unstable and existence of shadows furthermore interferences image processing and analysis. So some new algorithms of removing illumination influence should be explored to improve stability of vision navigation system. Human visual system has property of color constancy. How to introduce human color constancy into machine vision is a train of thought of solving illumination problem.
In the thesis, to realize autonomous navigation of agricultural robot in the field, illumination influence problem in vision navigation system was studied firstly with color constancy theories, a moblie robot platform for agriculture was designed, vision navigation system was constructed, and trials of crop line guidance and headland turn were carried through. Main research contents and conclusions include:
(1) Light microscopic influence on images was investigated through image fomation process. Theory of color constancy was introduced to solve illumination influence problem, and its current status was reviewed, two color constancy ways of solving illumination influence problem were concluded. One way is to take the conversion of images from in the non-canonical illumination to in the canonical illumination. Another way is to obtain illumination invariants.
(2) Some color constancy algorithms, which to take the conversion of images from in the non-canonical illumination to canonical illumination, were tested. These algorithms estimate illumination color globally and locally according to statistical information of image data, and then obtain color-corrected canonical images by von Kries model. A color learn based color image processing algorithm was proposed, Statistical results of image segmentation experiments to original images and canonical images showed, illumination variation influenced image segmentation quality, but canonical images under canonical illumination could improve segmentation results. Finally, according to time cost and segmentation effects, gray world algorithm and three local illumination estimation algorithm is equivalent in segmentation results, but gray world algorithm is high in efficiency and fit for real-time vision navigation, so gray world algorithm to obtain canonical image is chosen in later vision navigation system.
(3) Shadow formation process was analyzed. Theory of illumination invariant image was described. Based on this, a method of shadow removal was derived. To obtain the illumination invariant image, a camera-dependent illumination invariant angle should be measured. Aiming at solving this problem inconvenient, a method of minimal entropy was introduced. Results showed that method of shadow removal based on illumination invariant image could remove shadows efficiently. An enhanced OSTU threshold method was proposed to segment the illumination invariant image, and perfect effects were obtained.
(4) Based on founded model of farmland navigation working environment, navigation process of agricultural robot in the field was planed and main steps of navigation parameters extraction were analysised. An optimized Hough transform algorithm was proposed to extract guidance parameters for only single crop row. A line-scan algorithm with least squares theory was proposed to extract guidance parameters for double crop rows. A horizontal projection algorithm was used to detect headland. Tests of these image processing algorithms showed, all the algorithms were reasonable and reliable and total time cost of vision algorithms was enough for vision navigation.
(5) A CAN-based distributed control technology was introduced. A 4WD and 4WS robot platform was designed. A monocular vision navigation system was constructed. Multi-thread based vision navigation system software was developed. Validity and reliability of former vision algorithms were verified in the mobile robot platform. Results from cropline tracking tests showed that outdoor vision navigation system was feasible. In the end, an optical-encoder-based Dead Reckoning algorithm was proposed to fulfill headland turn function, and simulated tests showed the algorithm could suffice for headland turn.
引文
1 Naiqian Zhang, Maohua Wang, Ning Wang. Precision agriculture—a worldwide overview[J]. Computers and Electronics in Agriculture,2002,36:113-132.
2 Simon Blackmore, Bill Stout, Maohua Wang, et, al. Robotic Agriculture-The Future of Agricultural Mechanisation? [C].5th European Conference on Precision Agriculture,2005.
3姬长英.农业生产过程智能化的发展与展望[J].农业机械学报,1999,30(1)：106-109.
4赵匀,武传宇,胡旭东.农业机器人的研究进展及存在的问题[J],农业工程学报,2003,19(1)：20-24.
5张立彬,计时鸣,胥芳等.农业机器人的主要应用领域和关键技术[J],浙江工业大学学报,2002,30(1)：36-41.
6沈明霞.自主行走农业机器人视觉导航信息处理技术研究[D].南京：南京农业大学,2001.
7周俊.农用轮式移动机器人视觉导航系统的研究[D].南京：南京农业大学,2003.
8申川,蒋焕煜.机器视觉技术和GPS在农业车辆自动导航中的应用[J].农机化研究,2006,7：185-188.
9李建平,林妙玲.自动导航技术在农业工程中的应用研究进展[J].农机工程学报,2006,22(9)：232-236
10 John F. Reid. A Status Report on Autonomous Guidance of Agricultural Vehicles in the US:New Frontiers in the 21 st Century,1998, University of Illinois.
11 John F.Reid, Qin Zhang. Agricultural automatic guidance research in North America[J]. Computers and Electronics in Agriculture.2000.25:155-167.
12 O'Connor, Michael, Thomas Bell, et, al. Automatic steering of farm vehicles using GPS[C].3rd International Conference on Precision Agriculture, Minneapolis, MN.1996.
13 Thomas Bell. Automatic tractor guidance using carrier-phase differential GPS[J]. Computers and Electronics in Agriculture,2000,25:53-66.
14 http://waas.stanford.edu/pubs/index.htm.
15 M. Ollis, Stentz, A. Vision-Based Perception for an Autonomous Harvester[C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robotic Systems,1997,3:1838-1844.
16 Mark Ollis. Perception Algorithms for a Harvesting Robot [D]. The Robotics Institute Carnegie Mellon University,1997.
17 Gerrish, J.B, B.W. Fehr, G.R. Van Ee, et, al. Self-steering tractor guided by computer vision [J]. Applied Engineering in Agriculture,1997,13(5):559-563.
18 Ramalingam N, T S Stombaugh, J Mirgeaux. DGPS-Based Automatic Vehicle Guidance[C]. Annual International Meeting, Midwest Express Center, Milwaukee, Wisconsin, 2000.
19 Stombaugh, T. S., S. A. Shearer. DGPS-based guidance of high-speed application equipment[C]. Annual International Meeting, Sacramento Convention Center, Sacramento, California,2001.
20 Hart W E, J B Wilkerson, T F Morrow, et, al. Evaluating Operator Feedback Accuracy of Row-Guidance Systems with GPS[C]. The 6th International Conference on Precision Agriculture, Madison, WI,2002.
21 Vijay Subramanian, Thomas F Burks. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation[J]. Computers and Electronics in Agriculture,2006,53:130-143.
22 Lei Tian. Knowledge based machine vision system for outdoor plant Identification[D]. University of California Davis,1995.
23 http://age-web.age.uiuc.edu/faculty/qzhang/publications.asp
24Noguchi N, J F Reid, J Will, et, al. Vehicle automation system based on multi-sensor integration[C]. Annual International ASAE Meeting, MI,1998.
25Dickson M A, N Noguchi,Q Zhang, et, al. Sensor-fusion navigator for automated guidance of off-road vehicles. United States Patent No.6,445,983.
26 Qin Zhang. A Dynamic path search algorithm for tractor automatic navigation[J]. Transactions of the ASAE,2004,47(2):639-646.
27 Han S, Dickson M A,Ni B, et, al. A robust procedure to obtain a guidance directrix for vision based vehicle guidance system[C]. in Proceedings of the Automation Technology for Off-road Equipment, ASAE, Chicago, IL,2002.
28 Benson, T.S. Stombaugh, N. Noguchi, et, al. An evaluation of a geomagnetic direction sensor for vehicle guidance in precision agriculture applications[C]. Annual International ASAE Meeting, MI,1998.
29 Han S,Q Zhang, B Ni, et, al. Guidance directrix approach for designing a vision-based vehicle guidance system[J]. Computer and Electronics in Agriculture,2004,43(3):179-195.
30 Guo L, Y He, Q Zhang, et, al. Real-time tractor position estimation system using a Kalman filter[J]. Transactions of CSAE,2002,18(5):96-101.
31 Guo L, Q Zhang. Wireless data fusion system for agricultural vehicle positioning[J]. Biosystems Engineering,2005,91(3):261-269.
32 Kise M, Q Zhang, F Rovira Mas. A Stereovision-based crop row detector for tractor automated guidance[J]. Biosystems Engineering,2005,90(4):357-367.
33 Hong Chu Qiu. Navigation control for autonomous tractor guidance[D]. Illinois University,2002.
34 Wilson J N. Guidance of agricultural vehicles-a historical perspective[J]. Computers and Electronics in Agriculture,2000,25:3-9.
35 R. Keicher, H. Seufert. Automatic guidance for agricultural vehicles in Europe[J]. Computers and Electronics in Agriculture,2000,25:169-194.
36 Baerveldt A J. Guest Editorial:Special Issue on Agricultural Robotics[J]. Autonomous Robots, 2002,13(1):5-7.
37 R Keicher, H Seufert. Automatic guidance for agricultural vehicles in Europe[J]. Computers and Electronics in Agriculture,2000,25:169-194.
38 Gerhard Jahns. Automatic Guidance of Agricultural Field Machinery[J]. Institute for Biosystems Engineering, Federal Agricultural Research Centre (FAL), Germany.
39 Hague T, Tillett N D. Navigation and control of an autonomous horticultural robot[J]. Mechatronics, 1996,6:165-180.
40 C Debain. A guidance-assistance system for agricultural vehiclescComputers and Electronics in Agriculture,2000,25:29-51.
41 L CORDESSES. Combine Harvester Control Using Real Time Kinematic GPS[J]. Precision Agriculture,2000,2:147-161.
42 Lenain R, Thuilot B, Cariou C, et, al. Adaptive control for car like vehicles guidance relying on RTK GPS:rejection of sliding effects in agricultural applications[C]. In Proc. of the Intern. Conf. on Robotics and Automation (ICRA), Taipei,2003.
43 Roland Lenain. High accuracy path tracking for vehicles in presence of sliding:Application to farm vehicle automatic guidance for agricultural tasks[J]. Autonomous Robot,2006,
44 B THUILOT. Automatic Guidance of a Farm Tractor Relying on a Single CP-DGPS[J]. Autonomous Robots,2002,13:53-71.
45 Timo Oksanen, Markus Linja. Low-cost positioning system for agricultural vehicles[C]. Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation, Finland,2005.
46 H L Dijksterhuis, L G Van Willigenburg, R P Van Zuydam. Centimetre-precision guidance of moving implements in the open field:a simulation based on GPS measurements[J]. Computers and Electronics in Agriculture,1998,25:185-197.
47 B ASTRAND, ALBERT-JAN BAERVELDT. An agricultural mobile robot with vision-based perception for mechanical weed control[J]. Autonomous Robots,2002,13:21-35.
48 Tommy Johansson. Precise Navigation for an Agricultural Robot[D]. Department of Automatic Control Lund Institute of Technology in Sweden,2000.
49 http://www.danet.dk/.
50 M J.S(?)rensen. Modeling and control of wheeled farming robot[D]. Aalborg University, Demark, 2002.
51 M J.S(?)rensen. Modeling and control of wheeled farming robot[D]. Aalborg University, Demark, 2002.
52 Thomas Bak, Hans Jakobsen. Agricultural Robotic Platform with Four Wheel Steering for Weed Detection[J]. Biosystems Engineering,2004,87(2):125-136.
53 Blackmore B S, Griepentrog H W, Nielsen H, et, al. Development of a deterministic autonomous tractor[C]. CIGR International conference, Beijing,2004.
54 http://www.eco-dan.com.
55 http://www.FieldRobot.nl.
56 Billingsley J, Schoenfisch M. Vision-guidance of agricultural vehicles[C]. Proceedings of the 8th Australian Cotton Conference, Broadbeach, QLD,1996.
57 John Billingsley. Low cost GPS for Autonomous Robot Farmhand[C]. Mechatronics and Machine Vision in Practice, Hervey Bay, QLD, Australia,2000.
58 Toru Torii. Research in autonomous agriculture vehicles in Japan[J]. Computers and Electronics in Agriculture,2000,25:133-153.
59 Kazunobu Ishii. Navigation of an agricultural autonomous mobile robot[J]. Advanced Robotics, 1999,13(3):289-291.
60 Akira Mizushima, Noboru Noguchi. Development of Navigation Sensor Unit for the Agricultural Vehicle[C]. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2003.
61 Satoru Sakai. Global Performance of Agricultural Robots[C]. IEEE International Conference on Intelligent Robots and Systems, Japan,2004.
62 Yoshisada Nagasaka. Autonomous guidance for rice transplanting using global positioning and gyroscopes[J]. Computers and Electronics in Agriculture,2004,43:223-234.
63 Cho S I., Lee J H. Autonomous speed sprayer using differential global positioning system, genetic algorithm and fuzzy control [J]. Journal of Agriculture Engineering and Research,2000,76: 111-119.
64孙元义.机器视觉在棉田农药喷洒机中应用的研究[D].北京：中国农业大学,2006.
65袁佐云.基于计算机视觉的作物行中心线识别研究[D].北京：中国农业大学,2005.
66张卫.农业机器人视觉定位导航技术的研究[D].北京：中国农业大学,2006.
67赵颖.农业自主行走机器人视觉导航技术的研究[D].北京：中国农业大学,2006.
68张红霞.基于机器视觉的旱田多目标直线检测方法的研究[D].北京：中国农业大学,2007.
69张志斌.机器视觉导航技术在农用智能移动平台上的应用研究[D].广州：华南农业大学,2005.
70侯学贵.除草机器人杂草识别与视觉导航技术研究[D].南京：南京林业大学,2007.
71杨为民.农业机械机器视觉导航研究[D].西安：西安交通大学,2003.
72于国英.农业AGV视觉导航参数提取与轨迹控制的研究[D].镇江：江苏大学,2007.
73王荣本,纪寿文,初秀民等.基于机器视觉的玉米施肥智能机器系统设计概述[J].农业工程学报,2001,17(2)：0151-0153.
74冯雷.基于GPS和传感技术的农用车辆自动导航系统的研究[D].杭州：浙江大学,2004.
75张方明.田间路径识别算法和基于立体视觉的车辆自动导航方法研究[D].杭州：浙江大学,2006.
76 M J Swain, D H Ballard. Color Indexing[J]. International Journal of Computer Vision,1991,7(1): 11-32.
77欧宗瑛,袁野,张艳珍.基于颜色信息足球机器人视觉跟踪算法[J].大连理工大学学报,2000,(6)：729-732.
78陈建桦.非稳定光源下机器人足球员色彩区分的扰动分析与解决策略[D].国立高雄第一科技大学,2006.
79卢丽,刘万春,刘俐.基于彩色图像分割的机器人足球目标识别[J].计算机工程与设计,2004,25(11)：2093-2095.
80谭然.光强自适应的足球机器人视觉系统的设计研究[D].西安：西北工业大学,2007.
81 Kuo Yang Tu, Chien-Hua Chen. Analysis of camera's images influenced by light variation[C]. SICE Annual Conference, Kagawa University, Japan,2007.
82郭成果,熊蓉,褚健等.在不稳定光照条件下足球机器人视觉系统的设计[J].机器人,2006,2：200-205.
83周迪智,赵明国,石宗英等.RoboCup四腿组中颜色对光照不变性的研究.机器人.2007(6)： 586-589.
84 Nathan Lovell. Machine Vision as the Primary Sensory Input for Mobile Autonomous Robots[D]. Griffith University, Australia,2006.
85蔡殉.视频监控中的背景颜色恒常性算法研究[D].济南：山东大学,2007.
86 Juliana F Camapum, Mark H Fisher, Constantin F Hanes. Colour Segmentation and Recognition post Colour Constancy for Environmental Monitoring[C]. Proceedings Vision Interface'98, Vancouver, Canada.
87卿来云.人脸识别中的光照问题研究[D].中国科学院研究生院,2005.
88王涌.人脸识别中的光照处理算法研究[D].合肥：中国科学技术大学,2007.
89庄连生.复杂光照条件下人脸识别关键算法研究[D].合肥：中国科学技术大学,2006.
90朱进.人脸识别中光照影响的研究[D].长春：吉林大学,2007.
91徐贵力,毛罕平,李萍萍.缺素叶片彩色图像颜色特征提取的研究[J].农业工程学报,2002,4：150-154.
92武聪玲.基于计算机视觉的温室黄瓜幼苗营养无损监测研究[D].北京：中国农业大学,2005.
93李峥.基于计算机视觉的蔬菜颜色检测系统研究[D].长春：吉林大学,2004.
94邱道尹,张法全,张红涛.农田害虫色彩特征提取的研究[J].华北水利水电学院学报,2004,1：30-32.
95田有文,李成华.基于统计模式识别的植物病害彩色图像分割方法[J].沈阳农业大学学报,2003,4：301-304.
96陈坤杰,姬长英.牛肉自动分级技术研究进展分析[J].农业机械学报,2006,37(3)：153-157.
97应义斌,付峰.水果品质机器视觉检测中的图像颜色变换模型[J].农业机械学报,2004,35(1)：85-89.
98 Elfaki M S, N Zhang, D E Peterson. Factors affecting color-base weed detection[J]. Transactions of the ASAE,2000,43(4):1001-1009.
99钱丹.作物与杂草识别中图像分割影响因素的研究[D].镇江：江苏大学,2006.
100 D C Slaughter, D K Giles, D Downey. Autonomous robotic weed control systems:A review[J]. Computers and Electronics in Agriculture,2008,61:63-78.
101 Hemming J. Computer vision for identifying weeds in crops[D]. University of Hanover, Germany, 2000.
102 C M Onyango, J A Marchant. Physics-based colour image segmentation for scenes containing vegetation and soil[J]. Image and Vision Computing,2001,19:523-538.
103纪寿文.智能车辆在恶劣环境中自主导航方法的研究[D].长春：吉林大学,2001.
104高龙.CyberCar阴影路径识别算法研究[D].长春：吉林大学,2005.
105田磊.变光照条件CyberCar标示符识别算法研究[D].长春：吉林大学,2005.
106邢艳云.强光照条件下车道标识线识别与跟踪算法研究[D].长春：吉林大学,2006.
107赵佳佳.道路光照模式分类器设计[D].长春：吉林大学,2007.
108张俊.基于视觉的户外自主导航车辆的道路识别研究[D].西安：西安理工大学,2007.
1 Horn BKP. Robot Vision[M]. Cambridge:The MIT Press,1986.
2 金伟其.辐射度学光度与色度及其测量[M].北京：北京理工大学出版社,2006.
3 K Barnard. Practical colour constancy[D]. Simon Frasier University,1999.
4 Funt B, Barnard K, Martin L. Is machine colour constancy good enough? [C]. In Fifth European Conference on Computer Vision (ECCV'98), Freiburg, Germany,1998.
5 Finlayson GD, Hordley SD. Color constancy at a pixel[J]. Journal of the Optical Society of America,200,18(2):253-264.
6 Finlayson GD, Hordley SD, Drew MS. Removing shadows from images [C]. Proceedings of the European Conference on Computer Vision,2002.
7 Barnard K, Cardei V, Funt B. A comparison of computational color constancy algorithms-part I: methodology and experiments with synthesized data[J]. IEEE Transactions on Image Processing, 2002,11(9):972-984.
8 Forsyth DA. A novel approach to colour constancy[C]. Second International Conference on Computer Vision, Tampa, FL,1988.
9 曙光.彩色目标检测中的照明影响消除技术研究[D].哈尔滨工业大学,2003.
10杨莉.计算机视觉与生物视觉——颜色恒常性研究[D].中国科学院上海生命科学研究院,2006.
11 Land EH, McCann JJ. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971,61(1):1-11.
12 Land EH. The retinex theory of colour vision[J]. Proceedings of the Royal Institution of Great Britain,1974,47:23-58.
13 Land EH. Recent advances in retinex theory[J]. Vision Research,1986,26(1):7-21.
14蔡殉.视频监控中的背景颜色恒常性算法研究[D].济南：山东大学,2007.
15陶霖密,徐光祐.机器视觉中的颜色问题及应用[J].科学通报,2001,46(3)：178-190.
16 Finlayson GD. Color in perspective[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(10):1034-1038.
17 Finlayson GD, Hubel PM, Hordley S. Color by correlation[C]. The Fifth Color Imaging Conference: Color Science, Systems, and Applications, Scottsdale, AZ,1997.
18 Vlad Cardei. A Neural Network Approach To Colour Constancy [D]. Simon Fraser University in Canada,2000.
19 Finlayson GD, Schaefer G. Solving for colour constancy using a constrained dichromatic reflection model [J]. International Journal of Computer Vision,2001,42(3):127-144.
20 Klinker L T, Shafer S A, Kanade T. Using a color reflection model to separate highlights from object color[C]. In:Proceedings of IEEE International Conference on Computer Vision, London, England,1987.
21荆其诚,喻柏林,胡维生.色度学[M].北京：科学出版社,1979.
22 Ja'nos Schanda. Colorimetry:Understanding the CIE System[M]. John Wiley & Sons, Inc.,2007.
23 D B Judd, D L MacAdam, G Wyszecki. Spectral distribution of typical daylight as a function of correlated color temperature [J]. J. Opt. Soc. Amer.,1964,54:1031-1040.
24 Moritz Storring. Computer Vision and Human Skin Colour[D]. Aalborg University in Denmark, 2004.
25 Finlayson GD. Color constancy and a changing illumination[J]. SPIE,1994,2179:352-363.
26 Keisuke Takebe, Naoki Ito, Shigeki Nakauchi, et al. Digital Color Imaging with Color Constancy[J]. Systems and Computers in Japan,2003,34(1):79-88.
27 Noriyuki Shimano. Illuminant invariant recovery of surface reflectances and estimation of colorimetric valude from image signals[J]. Optical Review,1997,4(3):358-361.
28段华.室外移动机器人视觉导航关键技术研究[D].南京：南京航空航天大学,2006.
29刘关松,陆宗骐,徐建国等.几种彩色模型在不同光照条件下的稳定性分析[J].小型微型计算机系统,2002,23(7)：882-885.
30 Theo Gevers, Arnold W M Smeulders. Color-based object recognition [J]. Pattern Recognition, 1999,32(3):453-464.
1 林开颜,吴军辉,徐立鸿.彩色图像分割方法综述[J].中国图象图形学报,2005,10(1)：1-10.
2 黄庆明,张田文,潘少静.基于色彩学习的彩色图像分割方法[J].计算机研究与发展,1995,32(9)：60-64.
3 徐慧.Visual C++数字图像实用工程案例精选[M].北京：人民邮电出版社,2004.
4 Forsyth D A. A Novel Approach to Color Constancy [J]. International Journal of Computer Vision, 1990,5(1):5-36:
5 Barnard K, Cardei V, Funt B. A comparison of computational color constancy algorithms [J]. IEEE Transactions on Image Processing,2002,11(9):972-984.
6 朱贵科,沈理,王今觉.基于von-Kries色适应的分区颜色校正方法[J].计算机工程与科学,2007,29(2)：50-52.
7 Steven D Hordley. Scene Illuminant Estimation:Past, Present, and Future[J]. COLOR research and application,2006,31(4):303-314.
8 Finlayson GD, M Drew, Funt B. Diagonal transforms suffice for color constancy[C]. In IEEE International Conference on Computer Vision,1993.
9 Finlayson GD, S D Hordley, A Alsam. Investigating von Kries-like Adaptation Using Local Linear Models[J]. COLOR research and application,2006,31(2):90-101.
10 Kobus Barnard. Sensor Sharpening for Computational Color Constancy[J]. J. Opt. Soc. Am. A, 2001,18(11):2728-2743.
11 K.Barnard. Practical colour constancy[D]. Simon Frasier University,1999.
12 Ebner M. Combining white-patch retinex and the gray world assumption to achieve color constancy for multiple illuminants[C]. In Pattern Recognition, Proceedings of the 25th DAGM Symposium, Magdeburg, Germany,2003.
13 K Barnard, L Martin, B Funt. "A Data Set for Color Research," Color Research and Application, 2002,27(3):148-152.
14 http://www.cs.sfu.ca/～colour/data/
15 Finlayson GD, Schiele B, Crowley JL. Comprehensive colour image normalization[C]. In Fifth European Conference on Computer Vision (ECCV'98), Freiburg, Germany,1998.
16 Ebner M. A parallel algorithm for color constancy [J]. Journal of Parallel and Distributed Computing,2004,64(1):79-88.
17 Rafael C.Gonzalez. Digital Image Processing Using Matlab[M]. 北京:电子工业出版社,2004.
1 Finlayson GD, Hordley S. Color constancy at a pixel[J]. Journal of the Optical Society of America, 2001,28(2):253-264.
2 Chao Chen. Illumination Invariant Image Enhancement[D]. SIMON FRASER UNIVERSITY, 2002.
3 Finlayson GD, Hordley SD, Drew MS. Removing shadows from images[C]. Proceedings of the European Conference on Computer Vision,2002.
4 Finlayson GD, Hordley S, Lu C, et al. On the removal of shadows from images[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,2006,28(1):59-68.
5 Finlayson GD, Drew MS, Lu C. Intrinsic images by entropy minimization[C]. In Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic,2004.
6 Cheng Lu. Removing Shadows From Color Images[D]. SIMON FRASER UNIVERSITY,2006.
7 章毓晋.图像分割[M].北京：清华大学出版社,1999.
8 傅德胜,寿益禾.图形图像处理学[M].南京：东南大学出版社,2001.
1 申川,蒋焕煜.机器视觉技术和GPS在农业车辆自动导航中的应用[J].农机化研究,2006,(7)：185-188.
2 沈明霞.自主行走农业机器人视觉导航信息处理技术研究[D].南京：南京农业大学,2001.
3 赵颖.农业自主行走机器人视觉导航技术的研究[D].北京：中国农业大学,2006.
4 Rafael C.Gonzalez. Digital Image Processing Using Matlab[M].北京：电子工业出版社,2004.
5 Rafael C.Gonzalez.数字图像处理[M].阮秋琦译.北京：电子工业出版社,2005.
6 周俊.农用轮式移动机器人视觉导航系统的研究[D].南京：南京农业大学,2003.
7 赵颖,陈书旗.基于机器视觉的耕作机器人行走目标直线检测[J].农业机械学报,2006,37(4)：83-86.
8 V. Leemans, M.-F. Destain. Line cluster detection using a variant of the Hough transform for culture row localisation[J]. Image and Vision Computing,2006,24(5):541-550.
9 曾喆昭.数值计算[M].北京：清华大学出版社,2005.
10 H T S(?)gaard, H J Olsen. Determination of crop rows by image analysis without segmentation[J]. Computers and Electronics in Agriculture,2003,38:141-158.
11 袁佐云.基于计算机视觉的作物行中心线识别研究[D].北京：中国农业大学,2005.
12张红霞,张铁中.麦田多列目标图像检测算法[J].中国农业大学学报,2007,12(2)：62-66.
13张磊,王书茂,陈兵旗等.基于机器视觉的麦田边界检测[J].农业机械学报,2007,38(2)：111-114.
1 Simon Blackmore, Bill Stout, Wang Maohua, et al. Robotic agriculture-the future of agricultural mechanization?[C].5th European Conference on Precision Agriculture,2005.
2 Bark T, J Bendtsen, A P Ravn. Hybrid control design for a wheeled mobile robot[C]. Presented at Workshop on Hybrid systems, Denmark,2002.
3 Bak T, Jakobsen H. Agricultural robotic platform with four wheel steering for weed detection[J]. Biosyst. Eng.,2004,87(2):125-136.
4 杨树风.带有机械臂的全方位移动机器人的研制[D].哈尔滨：哈尔滨工业大学,2006.
5 张阳天,李保国,宗光华.低成本自主移动机器人平台的构建技术[J].航空制造技术,2006(11)：89-93.
6 Hermann Speckmann. Development and application of an agricultural bus for data transfer[J]. Computer and electronics in Agriculture,1999,23:219-237.
7 Matthew J Darr. CAN-based Distributed Control for Autonomous Vehicles[C]. ASAE/CSAE Annual International Meeting, BeiJing,2004.
8 谢斌,毛恩荣.拖拉机电液悬挂系统中CAN总线智能节点研究[J].农业机械学报,2006,37(12)：1-4.
9 杨晓华,侯巍,王树新等.基于CAN总线的水下机器人执行节点设计与实现[J].海洋技术,2005,24(4)：14-17.
10邬宽明.CAN总线原理和应用系统设计[M].北京：北京航空航天大学出版社,1996.
11饶运涛.现场总线CAN原理与应用技术[M].北京：北京航空航天大学出版社,2003.
12章毓晋.图象工程(下册)[M].北京：清华大学出版社,1999.
13付宜利,李寒.轮式全方位移动机器人几种转向方式的研究[J].制造业自动化,2005,27(10)：33-37.
14乔建行.软件系统集成[M].北京：科学出版社,2005.
15侯捷译.Win32多线程程序设计[M].武汉：华中科技大学出版社,2004.
16刘大维,王江涛等.多线程技术在自动引导车导航系统开发中的应用[J].河南科技大学学报.2007,28(3)：26-29.
17梁坤,王彬,蔡伯根.一种实用机器人定位系统的研究、实现与改进[J].电子机械工程.2006,22(4)：61-64.
18赵亦林著,谭国真译.车辆定位与导航系统[M].北京：电子工业出版社,1999.
2 Simon Blackmore, Bill Stout, Maohua Wang, et, al. Robotic Agriculture-The Future of Agricultural Mechanisation? [C].5th European Conference on Precision Agriculture,2005.
3姬长英.农业生产过程智能化的发展与展望[J].农业机械学报,1999,30(1)：106-109.
4赵匀,武传宇,胡旭东.农业机器人的研究进展及存在的问题[J],农业工程学报,2003,19(1)：20-24.
5张立彬,计时鸣,胥芳等.农业机器人的主要应用领域和关键技术[J],浙江工业大学学报,2002,30(1)：36-41.
6沈明霞.自主行走农业机器人视觉导航信息处理技术研究[D].南京：南京农业大学,2001.
7周俊.农用轮式移动机器人视觉导航系统的研究[D].南京：南京农业大学,2003.
8申川,蒋焕煜.机器视觉技术和GPS在农业车辆自动导航中的应用[J].农机化研究,2006,7：185-188.
9李建平,林妙玲.自动导航技术在农业工程中的应用研究进展[J].农机工程学报,2006,22(9)：232-236
10 John F. Reid. A Status Report on Autonomous Guidance of Agricultural Vehicles in the US:New Frontiers in the 21 st Century,1998, University of Illinois.
11 John F.Reid, Qin Zhang. Agricultural automatic guidance research in North America[J]. Computers and Electronics in Agriculture.2000.25:155-167.
12 O'Connor, Michael, Thomas Bell, et, al. Automatic steering of farm vehicles using GPS[C].3rd International Conference on Precision Agriculture, Minneapolis, MN.1996.
13 Thomas Bell. Automatic tractor guidance using carrier-phase differential GPS[J]. Computers and Electronics in Agriculture,2000,25:53-66.
14 http://waas.stanford.edu/pubs/index.htm.
15 M. Ollis, Stentz, A. Vision-Based Perception for an Autonomous Harvester[C]. Proceedings of the IEEE/RSJ International Conference on Intelligent Robotic Systems,1997,3:1838-1844.
16 Mark Ollis. Perception Algorithms for a Harvesting Robot [D]. The Robotics Institute Carnegie Mellon University,1997.
17 Gerrish, J.B, B.W. Fehr, G.R. Van Ee, et, al. Self-steering tractor guided by computer vision [J]. Applied Engineering in Agriculture,1997,13(5):559-563.
18 Ramalingam N, T S Stombaugh, J Mirgeaux. DGPS-Based Automatic Vehicle Guidance[C]. Annual International Meeting, Midwest Express Center, Milwaukee, Wisconsin, 2000.
19 Stombaugh, T. S., S. A. Shearer. DGPS-based guidance of high-speed application equipment[C]. Annual International Meeting, Sacramento Convention Center, Sacramento, California,2001.
20 Hart W E, J B Wilkerson, T F Morrow, et, al. Evaluating Operator Feedback Accuracy of Row-Guidance Systems with GPS[C]. The 6th International Conference on Precision Agriculture, Madison, WI,2002.
21 Vijay Subramanian, Thomas F Burks. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation[J]. Computers and Electronics in Agriculture,2006,53:130-143.
22 Lei Tian. Knowledge based machine vision system for outdoor plant Identification[D]. University of California Davis,1995.
23 http://age-web.age.uiuc.edu/faculty/qzhang/publications.asp
24Noguchi N, J F Reid, J Will, et, al. Vehicle automation system based on multi-sensor integration[C]. Annual International ASAE Meeting, MI,1998.
25Dickson M A, N Noguchi,Q Zhang, et, al. Sensor-fusion navigator for automated guidance of off-road vehicles. United States Patent No.6,445,983.
26 Qin Zhang. A Dynamic path search algorithm for tractor automatic navigation[J]. Transactions of the ASAE,2004,47(2):639-646.
27 Han S, Dickson M A,Ni B, et, al. A robust procedure to obtain a guidance directrix for vision based vehicle guidance system[C]. in Proceedings of the Automation Technology for Off-road Equipment, ASAE, Chicago, IL,2002.
28 Benson, T.S. Stombaugh, N. Noguchi, et, al. An evaluation of a geomagnetic direction sensor for vehicle guidance in precision agriculture applications[C]. Annual International ASAE Meeting, MI,1998.
29 Han S,Q Zhang, B Ni, et, al. Guidance directrix approach for designing a vision-based vehicle guidance system[J]. Computer and Electronics in Agriculture,2004,43(3):179-195.
30 Guo L, Y He, Q Zhang, et, al. Real-time tractor position estimation system using a Kalman filter[J]. Transactions of CSAE,2002,18(5):96-101.
31 Guo L, Q Zhang. Wireless data fusion system for agricultural vehicle positioning[J]. Biosystems Engineering,2005,91(3):261-269.
32 Kise M, Q Zhang, F Rovira Mas. A Stereovision-based crop row detector for tractor automated guidance[J]. Biosystems Engineering,2005,90(4):357-367.
33 Hong Chu Qiu. Navigation control for autonomous tractor guidance[D]. Illinois University,2002.
34 Wilson J N. Guidance of agricultural vehicles-a historical perspective[J]. Computers and Electronics in Agriculture,2000,25:3-9.
35 R. Keicher, H. Seufert. Automatic guidance for agricultural vehicles in Europe[J]. Computers and Electronics in Agriculture,2000,25:169-194.
36 Baerveldt A J. Guest Editorial:Special Issue on Agricultural Robotics[J]. Autonomous Robots, 2002,13(1):5-7.
37 R Keicher, H Seufert. Automatic guidance for agricultural vehicles in Europe[J]. Computers and Electronics in Agriculture,2000,25:169-194.
38 Gerhard Jahns. Automatic Guidance of Agricultural Field Machinery[J]. Institute for Biosystems Engineering, Federal Agricultural Research Centre (FAL), Germany.
39 Hague T, Tillett N D. Navigation and control of an autonomous horticultural robot[J]. Mechatronics, 1996,6:165-180.
40 C Debain. A guidance-assistance system for agricultural vehiclescComputers and Electronics in Agriculture,2000,25:29-51.
41 L CORDESSES. Combine Harvester Control Using Real Time Kinematic GPS[J]. Precision Agriculture,2000,2:147-161.
42 Lenain R, Thuilot B, Cariou C, et, al. Adaptive control for car like vehicles guidance relying on RTK GPS:rejection of sliding effects in agricultural applications[C]. In Proc. of the Intern. Conf. on Robotics and Automation (ICRA), Taipei,2003.
43 Roland Lenain. High accuracy path tracking for vehicles in presence of sliding:Application to farm vehicle automatic guidance for agricultural tasks[J]. Autonomous Robot,2006,
44 B THUILOT. Automatic Guidance of a Farm Tractor Relying on a Single CP-DGPS[J]. Autonomous Robots,2002,13:53-71.
45 Timo Oksanen, Markus Linja. Low-cost positioning system for agricultural vehicles[C]. Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation, Finland,2005.
46 H L Dijksterhuis, L G Van Willigenburg, R P Van Zuydam. Centimetre-precision guidance of moving implements in the open field:a simulation based on GPS measurements[J]. Computers and Electronics in Agriculture,1998,25:185-197.
47 B ASTRAND, ALBERT-JAN BAERVELDT. An agricultural mobile robot with vision-based perception for mechanical weed control[J]. Autonomous Robots,2002,13:21-35.
48 Tommy Johansson. Precise Navigation for an Agricultural Robot[D]. Department of Automatic Control Lund Institute of Technology in Sweden,2000.
49 http://www.danet.dk/.
50 M J.S(?)rensen. Modeling and control of wheeled farming robot[D]. Aalborg University, Demark, 2002.
51 M J.S(?)rensen. Modeling and control of wheeled farming robot[D]. Aalborg University, Demark, 2002.
52 Thomas Bak, Hans Jakobsen. Agricultural Robotic Platform with Four Wheel Steering for Weed Detection[J]. Biosystems Engineering,2004,87(2):125-136.
53 Blackmore B S, Griepentrog H W, Nielsen H, et, al. Development of a deterministic autonomous tractor[C]. CIGR International conference, Beijing,2004.
54 http://www.eco-dan.com.
55 http://www.FieldRobot.nl.
56 Billingsley J, Schoenfisch M. Vision-guidance of agricultural vehicles[C]. Proceedings of the 8th Australian Cotton Conference, Broadbeach, QLD,1996.
57 John Billingsley. Low cost GPS for Autonomous Robot Farmhand[C]. Mechatronics and Machine Vision in Practice, Hervey Bay, QLD, Australia,2000.
58 Toru Torii. Research in autonomous agriculture vehicles in Japan[J]. Computers and Electronics in Agriculture,2000,25:133-153.
59 Kazunobu Ishii. Navigation of an agricultural autonomous mobile robot[J]. Advanced Robotics, 1999,13(3):289-291.
60 Akira Mizushima, Noboru Noguchi. Development of Navigation Sensor Unit for the Agricultural Vehicle[C]. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2003.
61 Satoru Sakai. Global Performance of Agricultural Robots[C]. IEEE International Conference on Intelligent Robots and Systems, Japan,2004.
62 Yoshisada Nagasaka. Autonomous guidance for rice transplanting using global positioning and gyroscopes[J]. Computers and Electronics in Agriculture,2004,43:223-234.
63 Cho S I., Lee J H. Autonomous speed sprayer using differential global positioning system, genetic algorithm and fuzzy control [J]. Journal of Agriculture Engineering and Research,2000,76: 111-119.
64孙元义.机器视觉在棉田农药喷洒机中应用的研究[D].北京：中国农业大学,2006.
65袁佐云.基于计算机视觉的作物行中心线识别研究[D].北京：中国农业大学,2005.
66张卫.农业机器人视觉定位导航技术的研究[D].北京：中国农业大学,2006.
67赵颖.农业自主行走机器人视觉导航技术的研究[D].北京：中国农业大学,2006.
68张红霞.基于机器视觉的旱田多目标直线检测方法的研究[D].北京：中国农业大学,2007.
69张志斌.机器视觉导航技术在农用智能移动平台上的应用研究[D].广州：华南农业大学,2005.
70侯学贵.除草机器人杂草识别与视觉导航技术研究[D].南京：南京林业大学,2007.
71杨为民.农业机械机器视觉导航研究[D].西安：西安交通大学,2003.
72于国英.农业AGV视觉导航参数提取与轨迹控制的研究[D].镇江：江苏大学,2007.
73王荣本,纪寿文,初秀民等.基于机器视觉的玉米施肥智能机器系统设计概述[J].农业工程学报,2001,17(2)：0151-0153.
74冯雷.基于GPS和传感技术的农用车辆自动导航系统的研究[D].杭州：浙江大学,2004.
75张方明.田间路径识别算法和基于立体视觉的车辆自动导航方法研究[D].杭州：浙江大学,2006.
76 M J Swain, D H Ballard. Color Indexing[J]. International Journal of Computer Vision,1991,7(1): 11-32.
77欧宗瑛,袁野,张艳珍.基于颜色信息足球机器人视觉跟踪算法[J].大连理工大学学报,2000,(6)：729-732.
78陈建桦.非稳定光源下机器人足球员色彩区分的扰动分析与解决策略[D].国立高雄第一科技大学,2006.
79卢丽,刘万春,刘俐.基于彩色图像分割的机器人足球目标识别[J].计算机工程与设计,2004,25(11)：2093-2095.
80谭然.光强自适应的足球机器人视觉系统的设计研究[D].西安：西北工业大学,2007.
81 Kuo Yang Tu, Chien-Hua Chen. Analysis of camera's images influenced by light variation[C]. SICE Annual Conference, Kagawa University, Japan,2007.
82郭成果,熊蓉,褚健等.在不稳定光照条件下足球机器人视觉系统的设计[J].机器人,2006,2：200-205.
83周迪智,赵明国,石宗英等.RoboCup四腿组中颜色对光照不变性的研究.机器人.2007(6)： 586-589.
84 Nathan Lovell. Machine Vision as the Primary Sensory Input for Mobile Autonomous Robots[D]. Griffith University, Australia,2006.
85蔡殉.视频监控中的背景颜色恒常性算法研究[D].济南：山东大学,2007.
86 Juliana F Camapum, Mark H Fisher, Constantin F Hanes. Colour Segmentation and Recognition post Colour Constancy for Environmental Monitoring[C]. Proceedings Vision Interface'98, Vancouver, Canada.
87卿来云.人脸识别中的光照问题研究[D].中国科学院研究生院,2005.
88王涌.人脸识别中的光照处理算法研究[D].合肥：中国科学技术大学,2007.
89庄连生.复杂光照条件下人脸识别关键算法研究[D].合肥：中国科学技术大学,2006.
90朱进.人脸识别中光照影响的研究[D].长春：吉林大学,2007.
91徐贵力,毛罕平,李萍萍.缺素叶片彩色图像颜色特征提取的研究[J].农业工程学报,2002,4：150-154.
92武聪玲.基于计算机视觉的温室黄瓜幼苗营养无损监测研究[D].北京：中国农业大学,2005.
93李峥.基于计算机视觉的蔬菜颜色检测系统研究[D].长春：吉林大学,2004.
94邱道尹,张法全,张红涛.农田害虫色彩特征提取的研究[J].华北水利水电学院学报,2004,1：30-32.
95田有文,李成华.基于统计模式识别的植物病害彩色图像分割方法[J].沈阳农业大学学报,2003,4：301-304.
96陈坤杰,姬长英.牛肉自动分级技术研究进展分析[J].农业机械学报,2006,37(3)：153-157.
97应义斌,付峰.水果品质机器视觉检测中的图像颜色变换模型[J].农业机械学报,2004,35(1)：85-89.
98 Elfaki M S, N Zhang, D E Peterson. Factors affecting color-base weed detection[J]. Transactions of the ASAE,2000,43(4):1001-1009.
99钱丹.作物与杂草识别中图像分割影响因素的研究[D].镇江：江苏大学,2006.
100 D C Slaughter, D K Giles, D Downey. Autonomous robotic weed control systems:A review[J]. Computers and Electronics in Agriculture,2008,61:63-78.
101 Hemming J. Computer vision for identifying weeds in crops[D]. University of Hanover, Germany, 2000.
102 C M Onyango, J A Marchant. Physics-based colour image segmentation for scenes containing vegetation and soil[J]. Image and Vision Computing,2001,19:523-538.
103纪寿文.智能车辆在恶劣环境中自主导航方法的研究[D].长春：吉林大学,2001.
104高龙.CyberCar阴影路径识别算法研究[D].长春：吉林大学,2005.
105田磊.变光照条件CyberCar标示符识别算法研究[D].长春：吉林大学,2005.
106邢艳云.强光照条件下车道标识线识别与跟踪算法研究[D].长春：吉林大学,2006.
107赵佳佳.道路光照模式分类器设计[D].长春：吉林大学,2007.
108张俊.基于视觉的户外自主导航车辆的道路识别研究[D].西安：西安理工大学,2007.
1 Horn BKP. Robot Vision[M]. Cambridge:The MIT Press,1986.
2 金伟其.辐射度学光度与色度及其测量[M].北京：北京理工大学出版社,2006.
3 K Barnard. Practical colour constancy[D]. Simon Frasier University,1999.
4 Funt B, Barnard K, Martin L. Is machine colour constancy good enough? [C]. In Fifth European Conference on Computer Vision (ECCV'98), Freiburg, Germany,1998.
5 Finlayson GD, Hordley SD. Color constancy at a pixel[J]. Journal of the Optical Society of America,200,18(2):253-264.
6 Finlayson GD, Hordley SD, Drew MS. Removing shadows from images [C]. Proceedings of the European Conference on Computer Vision,2002.
7 Barnard K, Cardei V, Funt B. A comparison of computational color constancy algorithms-part I: methodology and experiments with synthesized data[J]. IEEE Transactions on Image Processing, 2002,11(9):972-984.
8 Forsyth DA. A novel approach to colour constancy[C]. Second International Conference on Computer Vision, Tampa, FL,1988.
9 曙光.彩色目标检测中的照明影响消除技术研究[D].哈尔滨工业大学,2003.
10杨莉.计算机视觉与生物视觉——颜色恒常性研究[D].中国科学院上海生命科学研究院,2006.
11 Land EH, McCann JJ. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971,61(1):1-11.
12 Land EH. The retinex theory of colour vision[J]. Proceedings of the Royal Institution of Great Britain,1974,47:23-58.
13 Land EH. Recent advances in retinex theory[J]. Vision Research,1986,26(1):7-21.
14蔡殉.视频监控中的背景颜色恒常性算法研究[D].济南：山东大学,2007.
15陶霖密,徐光祐.机器视觉中的颜色问题及应用[J].科学通报,2001,46(3)：178-190.
16 Finlayson GD. Color in perspective[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(10):1034-1038.
17 Finlayson GD, Hubel PM, Hordley S. Color by correlation[C]. The Fifth Color Imaging Conference: Color Science, Systems, and Applications, Scottsdale, AZ,1997.
18 Vlad Cardei. A Neural Network Approach To Colour Constancy [D]. Simon Fraser University in Canada,2000.
19 Finlayson GD, Schaefer G. Solving for colour constancy using a constrained dichromatic reflection model [J]. International Journal of Computer Vision,2001,42(3):127-144.
20 Klinker L T, Shafer S A, Kanade T. Using a color reflection model to separate highlights from object color[C]. In:Proceedings of IEEE International Conference on Computer Vision, London, England,1987.
21荆其诚,喻柏林,胡维生.色度学[M].北京：科学出版社,1979.
22 Ja'nos Schanda. Colorimetry:Understanding the CIE System[M]. John Wiley & Sons, Inc.,2007.
23 D B Judd, D L MacAdam, G Wyszecki. Spectral distribution of typical daylight as a function of correlated color temperature [J]. J. Opt. Soc. Amer.,1964,54:1031-1040.
24 Moritz Storring. Computer Vision and Human Skin Colour[D]. Aalborg University in Denmark, 2004.
25 Finlayson GD. Color constancy and a changing illumination[J]. SPIE,1994,2179:352-363.
26 Keisuke Takebe, Naoki Ito, Shigeki Nakauchi, et al. Digital Color Imaging with Color Constancy[J]. Systems and Computers in Japan,2003,34(1):79-88.
27 Noriyuki Shimano. Illuminant invariant recovery of surface reflectances and estimation of colorimetric valude from image signals[J]. Optical Review,1997,4(3):358-361.
28段华.室外移动机器人视觉导航关键技术研究[D].南京：南京航空航天大学,2006.
29刘关松,陆宗骐,徐建国等.几种彩色模型在不同光照条件下的稳定性分析[J].小型微型计算机系统,2002,23(7)：882-885.
30 Theo Gevers, Arnold W M Smeulders. Color-based object recognition [J]. Pattern Recognition, 1999,32(3):453-464.
1 林开颜,吴军辉,徐立鸿.彩色图像分割方法综述[J].中国图象图形学报,2005,10(1)：1-10.
2 黄庆明,张田文,潘少静.基于色彩学习的彩色图像分割方法[J].计算机研究与发展,1995,32(9)：60-64.
3 徐慧.Visual C++数字图像实用工程案例精选[M].北京：人民邮电出版社,2004.
4 Forsyth D A. A Novel Approach to Color Constancy [J]. International Journal of Computer Vision, 1990,5(1):5-36:
5 Barnard K, Cardei V, Funt B. A comparison of computational color constancy algorithms [J]. IEEE Transactions on Image Processing,2002,11(9):972-984.
6 朱贵科,沈理,王今觉.基于von-Kries色适应的分区颜色校正方法[J].计算机工程与科学,2007,29(2)：50-52.
7 Steven D Hordley. Scene Illuminant Estimation:Past, Present, and Future[J]. COLOR research and application,2006,31(4):303-314.
8 Finlayson GD, M Drew, Funt B. Diagonal transforms suffice for color constancy[C]. In IEEE International Conference on Computer Vision,1993.
9 Finlayson GD, S D Hordley, A Alsam. Investigating von Kries-like Adaptation Using Local Linear Models[J]. COLOR research and application,2006,31(2):90-101.
10 Kobus Barnard. Sensor Sharpening for Computational Color Constancy[J]. J. Opt. Soc. Am. A, 2001,18(11):2728-2743.
11 K.Barnard. Practical colour constancy[D]. Simon Frasier University,1999.
12 Ebner M. Combining white-patch retinex and the gray world assumption to achieve color constancy for multiple illuminants[C]. In Pattern Recognition, Proceedings of the 25th DAGM Symposium, Magdeburg, Germany,2003.
13 K Barnard, L Martin, B Funt. "A Data Set for Color Research," Color Research and Application, 2002,27(3):148-152.
14 http://www.cs.sfu.ca/～colour/data/
15 Finlayson GD, Schiele B, Crowley JL. Comprehensive colour image normalization[C]. In Fifth European Conference on Computer Vision (ECCV'98), Freiburg, Germany,1998.
16 Ebner M. A parallel algorithm for color constancy [J]. Journal of Parallel and Distributed Computing,2004,64(1):79-88.
17 Rafael C.Gonzalez. Digital Image Processing Using Matlab[M]. 北京:电子工业出版社,2004.
1 Finlayson GD, Hordley S. Color constancy at a pixel[J]. Journal of the Optical Society of America, 2001,28(2):253-264.
2 Chao Chen. Illumination Invariant Image Enhancement[D]. SIMON FRASER UNIVERSITY, 2002.
3 Finlayson GD, Hordley SD, Drew MS. Removing shadows from images[C]. Proceedings of the European Conference on Computer Vision,2002.
4 Finlayson GD, Hordley S, Lu C, et al. On the removal of shadows from images[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,2006,28(1):59-68.
5 Finlayson GD, Drew MS, Lu C. Intrinsic images by entropy minimization[C]. In Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic,2004.
6 Cheng Lu. Removing Shadows From Color Images[D]. SIMON FRASER UNIVERSITY,2006.
7 章毓晋.图像分割[M].北京：清华大学出版社,1999.
8 傅德胜,寿益禾.图形图像处理学[M].南京：东南大学出版社,2001.
1 申川,蒋焕煜.机器视觉技术和GPS在农业车辆自动导航中的应用[J].农机化研究,2006,(7)：185-188.
2 沈明霞.自主行走农业机器人视觉导航信息处理技术研究[D].南京：南京农业大学,2001.
3 赵颖.农业自主行走机器人视觉导航技术的研究[D].北京：中国农业大学,2006.
4 Rafael C.Gonzalez. Digital Image Processing Using Matlab[M].北京：电子工业出版社,2004.
5 Rafael C.Gonzalez.数字图像处理[M].阮秋琦译.北京：电子工业出版社,2005.
6 周俊.农用轮式移动机器人视觉导航系统的研究[D].南京：南京农业大学,2003.
7 赵颖,陈书旗.基于机器视觉的耕作机器人行走目标直线检测[J].农业机械学报,2006,37(4)：83-86.
8 V. Leemans, M.-F. Destain. Line cluster detection using a variant of the Hough transform for culture row localisation[J]. Image and Vision Computing,2006,24(5):541-550.
9 曾喆昭.数值计算[M].北京：清华大学出版社,2005.
10 H T S(?)gaard, H J Olsen. Determination of crop rows by image analysis without segmentation[J]. Computers and Electronics in Agriculture,2003,38:141-158.
11 袁佐云.基于计算机视觉的作物行中心线识别研究[D].北京：中国农业大学,2005.
12张红霞,张铁中.麦田多列目标图像检测算法[J].中国农业大学学报,2007,12(2)：62-66.
13张磊,王书茂,陈兵旗等.基于机器视觉的麦田边界检测[J].农业机械学报,2007,38(2)：111-114.
1 Simon Blackmore, Bill Stout, Wang Maohua, et al. Robotic agriculture-the future of agricultural mechanization?[C].5th European Conference on Precision Agriculture,2005.
2 Bark T, J Bendtsen, A P Ravn. Hybrid control design for a wheeled mobile robot[C]. Presented at Workshop on Hybrid systems, Denmark,2002.
3 Bak T, Jakobsen H. Agricultural robotic platform with four wheel steering for weed detection[J]. Biosyst. Eng.,2004,87(2):125-136.
4 杨树风.带有机械臂的全方位移动机器人的研制[D].哈尔滨：哈尔滨工业大学,2006.
5 张阳天,李保国,宗光华.低成本自主移动机器人平台的构建技术[J].航空制造技术,2006(11)：89-93.
6 Hermann Speckmann. Development and application of an agricultural bus for data transfer[J]. Computer and electronics in Agriculture,1999,23:219-237.
7 Matthew J Darr. CAN-based Distributed Control for Autonomous Vehicles[C]. ASAE/CSAE Annual International Meeting, BeiJing,2004.
8 谢斌,毛恩荣.拖拉机电液悬挂系统中CAN总线智能节点研究[J].农业机械学报,2006,37(12)：1-4.
9 杨晓华,侯巍,王树新等.基于CAN总线的水下机器人执行节点设计与实现[J].海洋技术,2005,24(4)：14-17.
10邬宽明.CAN总线原理和应用系统设计[M].北京：北京航空航天大学出版社,1996.
11饶运涛.现场总线CAN原理与应用技术[M].北京：北京航空航天大学出版社,2003.
12章毓晋.图象工程(下册)[M].北京：清华大学出版社,1999.
13付宜利,李寒.轮式全方位移动机器人几种转向方式的研究[J].制造业自动化,2005,27(10)：33-37.
14乔建行.软件系统集成[M].北京：科学出版社,2005.
15侯捷译.Win32多线程程序设计[M].武汉：华中科技大学出版社,2004.
16刘大维,王江涛等.多线程技术在自动引导车导航系统开发中的应用[J].河南科技大学学报.2007,28(3)：26-29.
17梁坤,王彬,蔡伯根.一种实用机器人定位系统的研究、实现与改进[J].电子机械工程.2006,22(4)：61-64.
18赵亦林著,谭国真译.车辆定位与导航系统[M].北京：电子工业出版社,1999.
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