中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 三维可视计算
Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models

文献类型：期刊论文

作者Zhao, Mingyang4,5; Jia, Xiaohong4,5; Fan, Lubin3; Liang, Yuan3; Yan, Dong-Ming1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2021
卷号30页码:3828-3843
关键词Kernel Robustness Optimization Gaussian mixture model Bandwidth Two dimensional displays Transforms Ellipse fitting GMM HGMM RANSAC outlier noise robust statistic
ISSN号1057-7149
DOI10.1109/TIP.2021.3065799
通讯作者Jia, Xiaohong(xhjia@amss.ac.cn)
英文摘要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.
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
语种英语
WOS记录号WOS:000634491000003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science of Foundation for Outstanding Young Scholars ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Program of China ; Beijing Advanced Discipline Fund ; Alibaba Group through Alibaba Innovative Research Program
源URL[http://ir.ia.ac.cn/handle/173211/44176]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Jia, Xiaohong
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100149, Peoples R China
3.Alibaba Grp, Hangzhou 311121, Peoples R China
4.Univ Chinese Acad Sci UCAS, Sch Math Sci, Beijing 100149, Peoples R China
5.Chinese Acad Sci, NCMIS Acad Math & Syst Sci, KLMM, Beijing 100190, 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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。