Epipolar geometry estimation for wide baseline stereo by Clustering Pairing Consensus

Authors:
Dazhi Zhang;Yongtao Wang;Wenbing Tao;Chengyi Xiong
Affiliations:
Institute for Pattern Recognition and Artificial Intelligence, School of Automation, National Key Laboratory of Science and Technology on Multi-spectral Information Processing Technologies, Huazho ...;Institute for Pattern Recognition and Artificial Intelligence, School of Automation, National Key Laboratory of Science and Technology on Multi-spectral Information Processing Technologies, Huazho ...;Institute for Pattern Recognition and Artificial Intelligence, School of Automation, National Key Laboratory of Science and Technology on Multi-spectral Information Processing Technologies, Huazho ...;College of Electronic and Information Engineering, Hubei Key Lab of Intelligent Wireless Communication, South-Central University for Nationalities, Wuhan 430074, China
Venue:
Pattern Recognition Letters
Year:
2014

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Abstract

The problem of automatic robust estimation of the epipolar geometry for wide-baseline image pair is difficult because the putative correspondences include a low percentage of inlier correspondences, and it could become a severe problem when the veridical data are themselves degenerate or near-degenerate. In this paper, Clustering Pairing Consensus (CPC) algorithm is proposed to estimate the fundamental matrix. The CPC algorithm first produces the Matched Regions Clusters (MRCs) using topological clustering (TC) algorithm given a scale parameter. An estimation is produced from each valid pair of MRCs and is then provided to M-estimation to compute a fundamental matrix. Finally, the best one is chosen as the final model from all the estimation. The proposed CPC algorithm has been demonstrated to be able to effectively estimate fundamental matrix and avoid the degeneracy of the traditional method for some difficult image pairs.