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基于分层神经网络带预测器的视觉跟踪 被引量：2

Visual Tracking with Predictor Based on Hierarchical Neural Network
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摘要 提出了一种新的视觉伺服控制方案 ,在该方案中利用视觉雅可比映射矩阵 ,求出了任务空间到视觉空间的视觉伺服控制方程 ,并利用这一方程建立了预测器 ,对物体图像的未来位置进行预测 ,这样就减小了图像处理的区域 ,提高了图像处理的速度 .同时 ,利用分层神经网络代替视觉空间到任务空间的映射 ,避免了复杂的逆矩阵计算 .仿真结果表明 ,这些方法的使用提高了图像处理的速度和跟踪精度 . A new controlling scheme of visual servoing is presented. By this scheme, a Jacobian mapping from task space to vision space is expressed and a visual servoing controlling is formulated. This formula is used to predict the future location of image of object. In this way the region of image processing is diminished and the speed of image processing is improved. In order to avoid the complex calculation of inverse Jacobian mapping, a hierarchical neural network is used to replace this calculation. Simulation results show that the speed of image processing and the precision of tracking is improved by these measured.
作者 席文明 朱剑英
机构地区 南京航空航天大学机电学院
出处 《西安交通大学学报》 EI CAS CSCD 北大核心 2002年第7期740-743,共4页 Journal of Xi'an Jiaotong University
基金 国家自然科学基金重大资助项目 (5 9990 4 70 )
关键词 预测器 视觉跟踪 视觉雅可比映射矩阵 分层神经网络 机器人视觉 视觉空间 visual tracking visual Jacobian mapping prediction hierarvhical neural network
分类号 TP242.62 [自动化与计算机技术—检测技术与自动化装置]
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参考文献8

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同被引文献30

  • 1蒋平,李自育,陈阳泉.迭代学习神经网络控制在机器人示教学习中的应用(英文)[J].控制理论与应用,2004,21(3):447-452. 被引量：8
  • 2Weiss L E,Sanderson A C.Image-based visual servo control using relational graph error signals.Proc.IEEE,1980:1074-1077.
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  • 7E Malis,F Chaumette,S Boudet.2D 1/2 visual servoing[J].IEEE Transactions on Robotics and Automation,1999.15(2):238-250.
  • 8Tsai R.A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J].IEEE Transactions on Robotics and Automation,1987.RA-3(4):323-344.
  • 9Mark B.Lynch,Cihan H.Dagli,Mahesh Vallenki.The use of feedforward neural networks for machine vision calibration[J].International Journal of Production Economics,1999,60/61:479-489.
  • 10Lyndon N.Smith,Melvyn L.Smith.Automatic machine vision calibration using statistical and neural network methods[J].Image and Vision Computing,2005,23(10):887-899.

引证文献2

二级引证文献7

西安交通大学学报
2002年 第7期

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