MA Shu-jun, JIN Tie-zheng, WANG Ying-lei, BAI Xin-hui. A Fast Tracking Strategy for Uncalibrated Visual Servo System[J]. Journal of Northeastern University Natural Science, 2020, 41(3): 355-360.
[1]Gong Z,Tao B,Yang H,et al.An uncalibrated visual servo method based on projective homography[J].IEEE Transactions on Automation Science and Engineering,2018,15(2):806-817. [2]梁新武.机械手无标定动态视觉伺服研究[D].武汉:华中科技大学,2011. (Liang Xin-wu.Research on uncalibrated dynamic visual servo of robot [D].Wuhan:Huazhong University of Science and Technology,2011.) [3]陶波,龚泽宇,丁汉.机器人无标定视觉伺服控制研究进展[J].力学学报,2016,48(4):767-783. (Tao Bo,Gong Ze-yu,Ding Han.Research progress of uncalibrated visual servo control for robots [J].Journal of Mechanics,2016,48 (4):767-783.) [4]Armstrong P J,Mcmurray G V,Lipkin H.A dynamic Jacobian estimation method for uncalibrated visual servoing[C]//IEEE/ASME International Conference on Advanced Intelligent Mechatronics.Atlanta,1999. [5]Bonkovic M,Ales H,Jezernik K.Population-based uncalibrated visual servoing[J].IEEE/ASME Transactions on Mechatronics,2008,13(3):393-397. [6]Hao M,Sun Z A. Universal state-space approach to uncalibrated model-free visual servoing[J].IEEE/ASME Transactions on Mechatronics,2012,17(5):833-846. [7]Music′ J,Bonkovic′ M,Cecis M.Comparison of uncalibrated model-free visual servoing methods[J].International Journal of Advanced Robotic Systems,2014,11(1):1-16. [8]Corke P I.The machine vision toolbox:a MATLAB toolbox for vision and vision-based control[J].IEEE Robotics & Automation Magazine,2005,12(4):16-25. [9]Corke P I.A robotics toolbox for MATLAB[J].IEEE Robotics & Automation Magazine,1996,3(1):24-32. [10]周丽,郭振民.试探性运动在无标定视觉伺服系统中的应用[J].哈尔滨理工大学学报,2002,7(1):11-17. (Zhou Li,Guo Zhen-min.Application of exploratory motion in uncalibrated visual servo system [J].Journal of Harbin University of Technology,2002,7(1):11-17.) [15]关守平,房少纯.一种新型的区间-粒子群优化算法[J].东北大学学报(自然科学版),2012,33(10):1381-1384.(Guan Shou-ping,Fang Shao-chun.A new interval particle swarm optimization algorithm[J].Journal of Northeastern University(Natural Science),2012,33(10):1381-1384.)(上接第320页)适应的直方图修改方法,能够在信息嵌入时避免过多修改带来模型失真,更适用于低嵌入量的情况.对比来说,文献 [12]的算法能满足更大的嵌入率要求.通栏表用“”表3本文算法与文献 [12]中算法的性能比较Table 3Performance comparison between the proposedalgorithm and the one in reference [12]网格模型文献 [12]ER/bpvSNR/dB本文 ER/bpvSNR/dBBunny0.28 55.65 0.38 58.91 Horse0.42 56.60 0.47 58.31 Armadillo0.14 59.43 0.21 76.67 Hand0.28 67.20 0.27 67.61 Dragon0.28 41.50 0.49 59.01 4结语本文提出了一种基于自适应直方图修改的网格可逆信息隐藏算法.该算法利用网格形状的局部相似性来预测顶点位置,使得构造的预测误差直方图更加陡峭,有利于增加嵌入容量和减少视觉失真.结合预测误差直方图的分布特点,直接使用嵌入区域内两组指定的预测误差来嵌入秘密信息,无需搜索可用的预测误差,减少了辅助信息对可用嵌入容量的占用.另外,根据载荷大小自适应动态选取合适的嵌入区域,有效避免对预测误差的过多移动,进一步减少了载密模型的视觉失真.实验结果表明,本文提出的算法能够在小容量嵌入时保持较高的视觉质量,适用于高保真的网格可逆信息隐藏.