求解相机自标定Kruppa方程的新方法

Lei Cheng, Fuchao Wu, Zhanyi Hu, H. Tsui
{"title":"求解相机自标定Kruppa方程的新方法","authors":"Lei Cheng, Fuchao Wu, Zhanyi Hu, H. Tsui","doi":"10.1109/ICPR.2002.1048301","DOIUrl":null,"url":null,"abstract":"We propose an approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt optimization or genetic optimization technique first. Then, the camera's intrinsic parameters are derived from the resulting linear constraints. Extensive simulations as well as experiments with real images verify that the above technique is both accurate and robust.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"A new approach to solving Kruppa equations for camera self-calibration\",\"authors\":\"Lei Cheng, Fuchao Wu, Zhanyi Hu, H. Tsui\",\"doi\":\"10.1109/ICPR.2002.1048301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt optimization or genetic optimization technique first. Then, the camera's intrinsic parameters are derived from the resulting linear constraints. Extensive simulations as well as experiments with real images verify that the above technique is both accurate and robust.\",\"PeriodicalId\":159502,\"journal\":{\"name\":\"Object recognition supported by user interaction for service robots\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Object recognition supported by user interaction for service robots\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2002.1048301\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Object recognition supported by user interaction for service robots","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2002.1048301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21

摘要

提出了一种求解相机自标定Kruppa方程的方法。传统的方法是先消除Kruppa方程中的未知尺度因子,从而得到一组非线性约束。相反,我们首先通过Levenberg-Marquardt优化或遗传优化技术确定比例因子。然后,由得到的线性约束导出相机的固有参数。大量的仿真和真实图像实验验证了上述技术的准确性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A new approach to solving Kruppa equations for camera self-calibration
We propose an approach to solving the Kruppa equations for camera self-calibration. Traditionally, the unknown scale factors in the Kruppa equations are eliminated first, leading to a set of nonlinear constraints. Instead, we determine the scale factors by a Levenberg-Marquardt optimization or genetic optimization technique first. Then, the camera's intrinsic parameters are derived from the resulting linear constraints. Extensive simulations as well as experiments with real images verify that the above technique is both accurate and robust.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Pattern recognition for humanitarian de-mining Data clustering using evidence accumulation Facial expression recognition using pseudo 3-D hidden Markov models Speeding up SVM decision based on mirror points Real-time tracking and estimation of plane pose
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1