OPT OpenIR  > 光电测量技术实验室
An optimized method based on ransac for fundamental matrix estimation
Wu, Wenjiang1,2; Liu, Wen1
2019-01-02
Conference Name2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018
Source Publication2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018
Pages372-376
Conference Date2018-07-13
Conference PlaceShenzhen, China
PublisherInstitute of Electrical and Electronics Engineers Inc.
Contribution Rank1
Abstract

Fundamental matrix estimation based on RANSAC will encounter the problems of computational inefficiency and low accuracy when outlier ratio is high. In this paper, an optimized method via modification of the RANSAC algorithm is proposed to solve these problems. First, an isolation forest-based algorithm is performed to detect outliers from putative SIFT correspondences according to distribution consistency of features in location, scale and orientation. Then, a number of obvious outliers are eliminated from putative correspondences, which will enhance the inlier ratio efficiently. Finally, fundamental matrix is estimated with the optimized set. Repeated experiments indicate that the proposed method has testified result in speed and accuracy. ? 2018 IEEE.

Department光电测量技术实验室
DOI10.1109/SIPROCESS.2018.8600471
Indexed ByEI
ISBN9781538663943
Language英语
EI Accession Number20190806536010
Citation statistics
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31293
Collection光电测量技术实验室
Affiliation1.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China;
2.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Wu, Wenjiang,Liu, Wen. An optimized method based on ransac for fundamental matrix estimation[C]:Institute of Electrical and Electronics Engineers Inc.,2019:372-376.
Files in This Item:
File Name/Size DocType Version Access License
An optimized method (2540KB)会议论文 暂不开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wu, Wenjiang]'s Articles
[Liu, Wen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wu, Wenjiang]'s Articles
[Liu, Wen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wu, Wenjiang]'s Articles
[Liu, Wen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.