Open AccessEfficient image features selection and weighting for fundamental matrix estimation
Author(s) -
Wang Liqiang,
Liu Zhen,
Zhang Zhonghua
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0436
Subject(s) - fundamental matrix (linear differential equation) , epipolar geometry , essential matrix , weighting , eight point algorithm , computer science , artificial intelligence , estimator , computer vision , affine transformation , matrix (chemical analysis) , mathematics , algorithm , pattern recognition (psychology) , image (mathematics) , symmetric matrix , statistics , medicine , materials science , composite material , radiology , mathematical analysis , eigenvalues and eigenvectors , physics , state transition matrix , quantum mechanics , pure mathematics
In computer vision, it is a challenge to compute the relationship of multiple views from scene images. The view relationship can be obtained from the fundamental matrix. Thus, it is very important to compute an accurate fundamental matrix from unevenly distributed features in complex scene images. This study proposes a robust method to estimate the fundamental matrix from corresponding images. First, the authors introduce how to find matched features from scene images efficiently. The epipolar geometry can restrict the point correspondences to the polar line, but cannot cope with the false points lying on the line. To eliminate such mismatches, the authors present an affine constraint which can also merge the uniform regions produced by mean‐shift segmentation. Second, inspired by the success of random sample consensus, the authors moderately improve the weighting function based on M‐estimator to increase the accuracy of the fundamental matrix estimation. Experimental results on simulated data and real images show these works are efficient for estimating fundamental matrix. The authors also evaluated the accuracy of their method on computing the external parameters of two cameras. The result shows that this method obtains comparable performance to the more sophisticated calibration method.