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Fast and Robust Algorithm for Fundamental Matrix Estimation

  • Ming Zhang
  • Guanghui WangEmail author
  • Haiyang Chao
  • Fuchao Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)

Abstract

Fundamental matrix estimation from two views plays an important role in 3D computer vision. In this paper, a fast and robust algorithm is proposed for the fundamental matrix estimation in the presence of outliers. Instead of algebra error, the reprojection error is adopted to evaluate the confidence of the fundamental matrix. Assuming Gaussian image noise, it is proved that the reprojection error can be described by a chi-square distribution, and thus, the outliers can be eliminated using the 3-sigma principle. With this strategy, the inlier set is robustly established in only two steps. Compared to classical RANSAC-based strategies, the proposed algorithm is very efficient with higher accuracy. Experimental evaluations and comparisons with previous methods demonstrate the effectiveness and advantages of the proposed approach.

Keywords

Fundamental matrix Robust estimation Outlier elimination 

Notes

Acknowledgment

The work is partly supported by the Kansas NASA EPSCoR Program, and the NSFC (61273282).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ming Zhang
    • 1
    • 2
  • Guanghui Wang
    • 1
    Email author
  • Haiyang Chao
    • 1
  • Fuchao Wu
    • 2
  1. 1.School of EngineeringUniversity of KansasLawrenceUSA
  2. 2.National Lab of Pattern RecognitionChinese Academy of SciencesBeijingChina

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