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Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion. Existing algorithms typically analyze the relative geometries of matched feature points identified in both projected views. Automated feature matching is itself a challenging problem. Results typically have a large number of false matches. Traditional fundamental matrix estimation methods are very sensitive to matching errors, which led naturally to the application of robust statistical estimation techniques to the problem. In this work, an entirely novel approach is proposed to the fundamental matrix estimation problem. Instead of analyzing the geometry of matched feature points, the problem is recast in the frequency domain through the use of Integral Projection, showing how this is a reasonable model for orthographic cameras. The problem now reduces to one of identifying matching lines in the frequency domain which, most importantly, requires no feature matching or correspondence information. Experimental results on both real and synthetic data are presented that demonstrate the algorithm is a practical technique for fundamental matrix estimation. The behavior of the proposed algorithm is additionally characterized with respect to input noise, feature counts, and other parameters of interest.
Computer vision, epipolar geometry, fundamental matrix, robust estimation, projection-slice theorem, Radon transformation.
Peter J. Kootsookos, John Williams, Andrew P. Bradley, Stefan Lehmann, I. Vaughan L. Clarkson, "Correspondence-Free Determination of the Affine Fundamental Matrix", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 82-97, January 2007, doi:10.1109/TPAMI.2007.5
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