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Improving ellipse fitting via multi-scale smoothing and key-point searching

文献类型：期刊论文

作者Chen, Xiao-Diao1,2; Qian, Cheng1; Zhao, Mingyang3; Yong, Jun -Hai4; Yan, Dong-Ming5,6
刊名PATTERN RECOGNITION
出版日期2024-07-01
卷号151页码:15
关键词Ellipse fitting Least-squares RANSAC Multi-scale smoothing Key-point searching
ISSN号0031-3203
DOI10.1016/j.patcog.2024.110432
通讯作者Zhao, Mingyang(zhaomingyang16@mails.ucas.ac.cn)
英文摘要Fast and efficient fitting of accurate ellipses from data points has many applications in pattern recognition, machine vision, and robotics. However, the fitting accuracy may significantly degrade in the existence of outliers, such as the least -squares -based approaches. Despite robust methods attaining more accurate results than the least -squares manner under the contamination of outliers, they typically require the careful tuning of the hyper -parameters for good results. To mitigate the outlier disturbance, in this paper, we propose a conceptually simple yet quite useful preprocessing framework for high -precision ellipse fitting. Firstly, we leverage multi -scale operators to shrink the input image, by which a large number of outliers can be removed, followed by the smoothing of the sub -image to further improve the data quality. Then, we propose a keypoint searching method to enhance the fitting precision via the analysis of the discrete pixel data in images. We prove that key -point -based ellipse fitting gives the upper bound of the approximation error generated by other sampled points with the same ellipse. Based on the key -point pairs inside and outside the ellipse, we further calculate their barycentric points and then perform fitting on these points to attain high -precision ellipses. We conduct extensive experiments on synthetic and real -world images to validate the proposed method and compare it with representative state-of-the-art approaches. Quantitative and qualitative results demonstrate that our method has more accurate and robust performance than competitors. Additionally, we employ the proposed method to compared approaches as a preprocessing step. Experiments demonstrate that our method is effective to significantly improve their fitting accuracy. Our source code is freely available at https://github.com/ChengQian09/MSKPF.
WOS关键词ROBUST ; ACCURACY ; DETECTOR
资助项目National Natural Science Foundation of China[62172415] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB0640000] ; Haihe Lab of ITAI Project[XCHK20210102]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001224648900001
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Haihe Lab of ITAI Project
源URL[http://ir.ia.ac.cn/handle/173211/58339]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Zhao, Mingyang
作者单位1.Hangzhou Dianzi Univ, Key Lab Complex Syst Modeling & Simulat, Hangzhou, Peoples R China
2.Haihe Lab ITAI, Tianjin, Peoples R China
3.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot CAIR, Hong Kong, Peoples R China
4.Tsinghua Univ, Sch Software, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, MAIS, Beijing, Peoples R China
6.Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Xiao-Diao,Qian, Cheng,Zhao, Mingyang,et al. Improving ellipse fitting via multi-scale smoothing and key-point searching[J]. PATTERN RECOGNITION,2024,151:15.
APA Chen, Xiao-Diao,Qian, Cheng,Zhao, Mingyang,Yong, Jun -Hai,&Yan, Dong-Ming.(2024).Improving ellipse fitting via multi-scale smoothing and key-point searching.PATTERN RECOGNITION,151,15.
MLA Chen, Xiao-Diao,et al."Improving ellipse fitting via multi-scale smoothing and key-point searching".PATTERN RECOGNITION 151(2024):15.

入库方式： OAI收割

来源：自动化研究所

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