Knowledge Commons of Institute of Automation,CAS
Conic Fitting: New Easy Geometric Method and Revisiting Sampson Distance | |
Wu,Yihong1,2; Wang,Haoren1,2; Tang,Fulin1,2 | |
2017 | |
会议名称 | 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) |
会议日期 | 2017-11-26 |
会议地点 | Nanjing, China |
出版者 | IEEE |
摘要 | Fitting conic from images is a preliminary step for its plentiful applications. It's a common sense that geometric distance based fitting methods are better than algebraic distance based ones. However, for a long time, there has not been a geometric distance between a data point and a general conic that allows easy computation and achieves high accuracy simultaneously. In this paper, we derive a new geometric distance between a data point and a conic by revisiting Sampson distance. The new geometric distance is accurate and simultaneously still explicit analytical representation, which is greatly easy to be implemented. Then, based on the distance, a new cost function with combining Sampson distance is constructed. The conic fitting optimization by minimizing this cost function has all the merits of the geometric distance based methods and simultaneously avoids their limitations. |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23622 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
通讯作者 | Wu,Yihong |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wu,Yihong,Wang,Haoren,Tang,Fulin. Conic Fitting: New Easy Geometric Method and Revisiting Sampson Distance[C]:IEEE,2017. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Wu_et_al_2017_Conic_(390KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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