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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 672509, 5 pages
Research Article

Reliable RANSAC Using a Novel Preprocessing Model

1School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Received 8 December 2012; Revised 8 January 2013; Accepted 17 January 2013

Academic Editor: Carlo Cattani

Copyright © 2013 Xiaoyan Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.