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Increasing the Accuracy for Computer Vision Systems by Removing the Noise
Wang ZZ(王振洲)
Department机器人学研究室
Conference Name3rd IEEE International Conference on Control, Automation and Robotics (ICCAR)
Conference DateApril 22-24, 2017
Conference PlaceNagoya, JAPAN
Author of SourceIEEE
Source Publication2017 3RD INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR)
PublisherIEEE
Publication PlaceNEW YORK
2017
Pages371-374
Indexed ByEI ; CPCI(ISTP)
EI Accession number20172803940162
WOS IDWOS:000404256400070
Contribution Rank1
ISBN978-1-5090-6088-7
KeywordDegree Of Freedom Noise Pattern Modeling Computer Vision
AbstractRobots 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/20719
Collection机器人学研究室
Corresponding AuthorWang ZZ(王振洲)
AffiliationState Key Lab for Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China
Recommended Citation
GB/T 7714
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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