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RES-Q: Robust Outlier Detection Algorithm for Fundamental Matrix Estimation

文献类型：期刊论文

作者Bharati, Sushil Pratap2; Cen, Feng3; Sharda, Ajay1; Wang, Guanghui2,4
刊名IEEE ACCESS
出版日期2018
卷号6页码:48664-48674
关键词Fundamental Matrix Stereo Vision Robust Statistics Outliers Detection
ISSN号2169-3536
DOI10.1109/ACCESS.2018.2867915
文献子类Article
英文摘要Detection of outliers present in noisy images for an accurate fundamental matrix estimation is an important research topic in the field of 3-D computer vision. Although a lot of research is conducted in this domain, not much study has been done in utilizing the robust statistics for successful outlier detection algorithms. This paper proposes to utilize a reprojection residual error-based technique for outlier detection. Given a noisy stereo image pair obtained from a pair of stereo cameras and a set of initial point correspondences between them, reprojection residual error and 3-sigma principle together with robust statistic-based Qn estimator (RES-Q) is proposed to efficiently detect the outliers and estimate the fundamental matrix with superior accuracy. The proposed RES-Q algorithm demonstrates greater precision and lower reprojection residual error than the state-of-the-art techniques. Moreover, in contrast to the assumption of Gaussian noise or symmetric noise model adopted by most previous approaches, the RES-Q is found to be robust for both symmetric and asymmetric random noise assumptions. The proposed algorithm is experimentally tested on both synthetic and real image data sets, and the experiments show that RES-Q is more effective and efficient than the classical outlier detection algorithms.
WOS关键词INTELLIGENT VEHICLES ; EPIPOLAR GEOMETRY ; SAMPLE CONSENSUS ; FACTORIZATION ; REGISTRATION ; RECOGNITION ; REMOVAL ; VISION ; MOTION
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000445484000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Kansas NASA EPSCoR Program(KNEP-PDG-10-2017-KU) ; United States Department of Agriculture (USDA)(USDA 2017-67007-26153) ; General Research Fund of the University of Kansas(2228901) ; National Natural Science Foundation of China(61573351)
源URL[http://ir.ia.ac.cn/handle/173211/27908]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者Wang, Guanghui
作者单位1.Kansas State Univ, Biol & Agr Engn, Manhattan, KS 66506 USA
2.Univ Kansas, Dept Elect Engn & Comp Sci, Lawrence, KS 66045 USA
3.Tongji Univ, Coll Elect & Informat Engn, Dept Control Sci & Engn, Shanghai 200092, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Bharati, Sushil Pratap,Cen, Feng,Sharda, Ajay,et al. RES-Q: Robust Outlier Detection Algorithm for Fundamental Matrix Estimation[J]. IEEE ACCESS,2018,6:48664-48674.
APA Bharati, Sushil Pratap,Cen, Feng,Sharda, Ajay,&Wang, Guanghui.(2018).RES-Q: Robust Outlier Detection Algorithm for Fundamental Matrix Estimation.IEEE ACCESS,6,48664-48674.
MLA Bharati, Sushil Pratap,et al."RES-Q: Robust Outlier Detection Algorithm for Fundamental Matrix Estimation".IEEE ACCESS 6(2018):48664-48674.

入库方式： OAI收割

来源：自动化研究所

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