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Increasing the Accuracy for Computer Vision Systems by Removing the Noise
Wang ZZ(王振洲)
作者部门机器人学研究室
会议名称3rd IEEE International Conference on Control, Automation and Robotics (ICCAR)
会议日期April 22-24, 2017
会议地点Nagoya, JAPAN
会议主办者IEEE
会议录名称2017 3RD INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR)
出版者IEEE
出版地NEW YORK
2017
页码371-374
收录类别EI ; CPCI(ISTP)
EI收录号20172803940162
WOS记录号WOS:000404256400070
产权排序1
ISBN号978-1-5090-6088-7
关键词Degree Of Freedom Noise Pattern Modeling Computer Vision
摘要Robots rely on the computer vision systems to obtain the environmental information. As a result, the accuracy of the computer vision systems is essential for the control of the robots. Many computer vision systems make use of markers of the well-designed patterns to calculate the system parameters. Undesirably, the noise exists universally, which decreases the calibration accuracy and consequently decreases the accuracy of the computer vision systems. In this paper, we propose a pattern modeling method to remove the noise by decreasing the degree of freedom of the total calibration markers to one. The theorem is proposed and proved. The proposed method can be readily adopted by different computer vision systems, e.g. structured light based computer vision systems and stereo vision based systems.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/20719
专题机器人学研究室
通讯作者Wang ZZ(王振洲)
作者单位State Key Lab for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
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Wang ZZ. Increasing the Accuracy for Computer Vision Systems by Removing the Noise[C]//IEEE. NEW YORK:IEEE,2017:371-374.
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