Reliable RANSAC Using a Novel Preprocessing Model
Joint Authors
Liu, Sheng
Zhang, Hui
Wang, Xiaoyan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-02-20
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
Geometric assumption and verification with RANSAC has become a crucial step for corresponding to local features due to its wide applications in biomedical feature analysis and vision computing.
However, conventional RANSAC is very time-consuming due to redundant sampling times, especially dealing with the case of numerous matching pairs.
This paper presents a novel preprocessing model to explore a reduced set with reliable correspondences from initial matching dataset.
Both geometric model generation and verification are carried out on this reduced set, which leads to considerable speedups.
Afterwards, this paper proposes a reliable RANSAC framework using preprocessing model, which was implemented and verified using Harris and SIFT features, respectively.
Compared with traditional RANSAC, experimental results show that our method is more efficient.
American Psychological Association (APA)
Wang, Xiaoyan& Zhang, Hui& Liu, Sheng. 2013. Reliable RANSAC Using a Novel Preprocessing Model. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-5.
https://search.emarefa.net/detail/BIM-489295
Modern Language Association (MLA)
Wang, Xiaoyan…[et al.]. Reliable RANSAC Using a Novel Preprocessing Model. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-5.
https://search.emarefa.net/detail/BIM-489295
American Medical Association (AMA)
Wang, Xiaoyan& Zhang, Hui& Liu, Sheng. Reliable RANSAC Using a Novel Preprocessing Model. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-5.
https://search.emarefa.net/detail/BIM-489295
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-489295