KMS Of Academy of mathematics and systems sciences, CAS
| Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models | |
| Zhao, Mingyang1,2; Jia, Xiaohong1,2; Fan, Lubin3; Liang, Yuan3; Yan, Dong-Ming4,5 | |
| 2021 | |
| Source Publication | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| ISSN | 1057-7149 |
| Volume | 30Pages:3828-3843 |
| Abstract | 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. |
| Keyword | 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 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000634491000003 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/58412 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Jia, Xiaohong |
| Affiliation | 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 |
| Recommended Citation 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. |
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