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Coherent chord computation and cross ratio for accurate ellipse detection

文献类型：期刊论文

作者Zhao, Mingyang4,5,6; Jia, Xiaohong3; Ma, Lei2,6; Hu, Li-Ming1; Yan, Dong-Ming1,4,5
刊名PATTERN RECOGNITION
出版日期2024-02-01
卷号146页码:16
ISSN号0031-3203
关键词Ellipse detection Chord computation Cross ratio Hough transform
DOI10.1016/j.patcog.2023.109983
通讯作者Jia, Xiaohong(xhjia@amss.ac.cn) ; Ma, Lei(lei.ma@pku.edu.cn)
英文摘要This paper presents a new method for detecting ellipses in images, which has many applications in pattern recognition and robotic tasks. Previous approaches typically use sophisticated arc grouping strategies or calculate differential such as tangents, and thereby they are less efficient or more sensitive to noise. In this work, we present a novel ellipse detector, based on the simple yet effective chord computation, and on the projective invariant cross ratio, which achieves promising performance in both accuracy and efficiency. First, elliptical arcs are extracted by fast vector computations along with the removal of straight segments to speed up detection. Then, arcs from the same ellipse are grouped together according to the relative location and the intersecting chord constraints, both are on coherent chord computation without differential. Additionally, an efficient additive principle is applied to further accelerate the grouping process. Finally, a novel and robust verification by area-deduced cross ratio is introduced to pick out salient ellipses. Compared with predecessor methods, cross ratio is not only simple for computation, but also has invariant properties (used to discriminate ellipses). Extensive experiments on seven public datasets (including synthetic and real-world images) are implemented. The results highlight the salient advantages of the proposed method compared to state-of-theart detectors: Easier to implementation, more robust against occlusion and noise, as well as attaining higher F-measure.
资助项目Na-tional Key Research and Development Program of China[2020YFB1708900] ; National Natural Science Foundation of China[12022117] ; National Natural Science Foundation of China[62172415] ; CAS Project for Young Scientists in Basic Research[YSBR-034] ; Open Research Fund Program of State key Laboratory of Hydroscience and Engineering, Tsinghua University[sklhse-2022-D-04]
WOS研究方向Computer Science ; Engineering
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:001088526900001
资助机构Na-tional Key Research and Development Program of China ; National Natural Science Foundation of China ; CAS Project for Young Scientists in Basic Research ; Open Research Fund Program of State key Laboratory of Hydroscience and Engineering, Tsinghua University
源URL[http://ir.ia.ac.cn/handle/173211/54304]  
专题多模态人工智能系统全国重点实验室
模式识别国家重点实验室_三维可视计算
通讯作者Jia, Xiaohong; Ma, Lei
作者单位1.Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing, Peoples R China
2.Peking Univ, Coll Future Technol, Natl Biomed Imaging Ctr, Beijing, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, KLMM, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, MAIS, Beijing, Peoples R China
6.Beijing Acad Artificial Intelligence, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Mingyang,Jia, Xiaohong,Ma, Lei,et al. Coherent chord computation and cross ratio for accurate ellipse detection[J]. PATTERN RECOGNITION,2024,146:16.
APA Zhao, Mingyang,Jia, Xiaohong,Ma, Lei,Hu, Li-Ming,&Yan, Dong-Ming.(2024).Coherent chord computation and cross ratio for accurate ellipse detection.PATTERN RECOGNITION,146,16.
MLA Zhao, Mingyang,et al."Coherent chord computation and cross ratio for accurate ellipse detection".PATTERN RECOGNITION 146(2024):16.

入库方式： OAI收割

来源：自动化研究所

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