[1] 马庭田. 基于机器人视觉的多类型工件识别与定位问题研究[D]. 南京: 南京航空航天大学, 2018: 8- 18.

    Ma TT. Research on identification and positioning problems for a variety of work pieces on binocular stereo vision[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018: 8- 18.

[2] 李双. 基于机器视觉的叶片夹取机器人研究[D]. 哈尔滨: 东北林业大学, 2018: 42- 47.

    LiS. Research on blade recognition robot based on machine vision[D]. Harbin: Northeast Forestry University, 2018: 42- 47.

[3] 桑孟雷. 基于机器视觉的圆形工件位姿估计[D]. 杭州: 浙江大学, 2018: 11- 24.

    Sang ML. Machine vision based location and attitude estimation for circular workpiece[D]. Hangzhou: Zhejiang University, 2018: 11- 24.

[4] Zhang Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.

[5] Tsai R. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE Journal on Robotics and Automation, 1987, 3(4): 323-344.

[6] 解则晓, 张安祺. 超大尺度线结构光传感器内外参数同时标定[J]. 光学学报, 2018, 38(3): 0315001.

    Xie Z X, Zhang A Q. Simultaneous calibration of the intrinsic and extrinsic parameters of ultra-large-scale line structured-light sensor[J]. Acta Optica Sinica, 2018, 38(3): 0315001.

[7] 游江, 唐力伟, 邓士杰. 基于改进遗传模拟退火的相机标定方法[J]. 计算机工程与设计, 2017, 38(3): 819-824.

    You J, Tang L W, Deng S J. Camera calibration method based on genetic algorithm and simulated annealing[J]. Computer Engineering and Design, 2017, 38(3): 819-824.

[8] Zhang Z, Zhao R J, Liu E H, et al. A single-image linear calibration method for camera[J]. Measurement, 2018, 130: 298-305.

[9] 黄伟光, 董安国. 基于粒子群算法的摄像机自标定[J]. 计算机应用与软件, 2015, 32(5): 216-219, 233.

    Huang W G, Dong A G. Camera self-calibration based on particle swarm optimisation[J]. Computer Applications and Software, 2015, 32(5): 216-219, 233.

[10] 郭彤颖, 李宁宁, 刘雍. 基于粒子群算法的摄像机内参数优化方法[J]. 激光与光电子学进展, 2017, 54(11): 111504.

    Guo T Y, Li N N, Liu Y. Optimization of camera internal parameters based on particle swarm algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111504.

[11] 王道累, 胡松. 基于量子粒子群优化算法的摄像机标定优化方法[J]. 激光与光电子学进展, 2018, 55(12): 121502.

    Wang D L, Hu S. Optimization method of camera calibration based on quantum-behaved particle swarm optimization algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121502.

[12] 秦瑞康, 杨月全, 李福东, 等. 基于全参数自适应变异粒子群算法的单目相机标定[J]. 东南大学学报(自然科学版), 2017, 47(z1): 193-198.

    Qin R K, Yang Y Q, Li F D, et al. Monocular camera calibration based on particle swarm algorithm with all parameter adaptive mutation mechanism[J]. Journal of Southeast University(Natural Science Edition), 2017, 47(z1): 193-198.

[13] 李少波, 张成龙, 郑凯. 基于改进粒子群算法的制造车间WSN部署优化[J]. 仪表技术与传感器, 2017( 10): 101- 104.

    Li SB, Zhang CL, ZhengK. Node deployments optimization in wireless sensor networks based on improved particle swarm algorithm[J]. Instrument Technique and Sensor, 2017( 10): 101- 104.

[14] 赵丰明, 樊艳芳, 钱福如, 等. 关于新能源并网供电无功性能优化设计研究[J]. 计算机仿真, 2018, 35(11): 118-122.

    Zhao F M, Fan Y F, Qian F R, et al. Research on optimized design of reactive power performance of new energy grid connected power supply simplified particle swarm optimization[J]. Computer Simulation, 2018, 35(11): 118-122.

[15] 杜江, 袁中华, 王景芹. 动态改变惯性权重的新模式粒子群算法[J]. 安徽大学学报(自然科学版), 2018, 42(2): 60-66.

    Du J, Yuan Z H, Wang J Q. New model of particle swarm optimization algorithm with dynamically changing inertia weight[J]. Journal of Anhui University(Natural Science Edition), 2018, 42(2): 60-66.

[16] 游佳丽, 周志勇, 章程, 等. 基于自适应驱散机制的粒子群优化算法[J]. 计算机工程与应用, 2017, 53(7): 41-48, 103.

    You J L, Zhou Z Y, Zhang C, et al. Adaptive dispersion mechanism based particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2017, 53(7): 41-48, 103.