Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models

doi:10.1109/TIP.2021.3065799

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	Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models
	Zhao, Mingyang 1,2; Jia, Xiaohong 1,2; Fan, Lubin 3; Liang, Yuan 3; Yan, Dong-Ming 4,5
	2021
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	30 页码:3828-3843
摘要	Fitting ellipses from unrecognized data is a fundamental problem in computer vision and pattern recognition. Classic least-squares based methods are sensitive to outliers. To address this problem, in this paper, we present a novel and effective method called hierarchical Gaussian mixture models (HGMM) for ellipse fitting in noisy, outliers-contained, and occluded settings on the basis of Gaussian mixture models (GMM). This method is crafted into two layers to significantly improve its fitting accuracy and robustness for data containing outliers/noise and has been proven to effectively narrow down the iterative interval of the kernel bandwidth, thereby speeding up ellipse fitting. Extensive experiments are conducted on synthetic data including substantial outliers (up to 60%) and strong noise (up to 200%) as well as on real images including complex benchmark images with heavy occlusion and images from versatile applications. We compare our results with those of representative state-of-the-art methods and demonstrate that our proposed method has several salient advantages, such as its high robustness against outliers and noise, high fitting accuracy, and improved performance.
关键词	Kernel Robustness Optimization Gaussian mixture model Bandwidth Two dimensional displays Transforms Ellipse fitting GMM HGMM RANSAC outlier noise robust statistic
DOI	10.1109/TIP.2021.3065799
收录类别	SCI
语种	英语
资助项目	National Natural Science of Foundation for Outstanding Young Scholars[12022117] ; National Natural Science Foundation of China[61872354] ; National Natural Science Foundation of China[61772523] ; Beijing Natural Science Foundation[Z190004] ; Beijing Natural Science Foundation[L182059] ; National Key Research and Development Program of China[2020YFB1708900] ; National Key Research and Development Program of China[2019YFB2204104] ; Beijing Advanced Discipline Fund[115200S001] ; Alibaba Group through Alibaba Innovative Research Program
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000634491000003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58412
专题	中国科学院数学与系统科学研究院
通讯作者	Jia, Xiaohong
作者单位	1.Chinese Acad Sci, NCMIS Acad Math & Syst Sci, KLMM, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci UCAS, Sch Math Sci, Beijing 100149, Peoples R China 3.Alibaba Grp, Hangzhou 311121, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100149, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Mingyang,Jia, Xiaohong,Fan, Lubin,et al. Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:3828-3843.
APA	Zhao, Mingyang,Jia, Xiaohong,Fan, Lubin,Liang, Yuan,&Yan, Dong-Ming.(2021).Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,3828-3843.
MLA	Zhao, Mingyang,et al."Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):3828-3843.