  The technique requires the epipolar geometry to be pre-estimated between each
image pair. It exploits the constraints which the camera movement implies, in
order to apply a closed-form correction to the parameters of the input
affinities. Also, it is shown that the rotations and scales obtained by
partially affine-covariant detectors, e.g., AKAZE or SIFT, can be completed to
be full affine frames by the proposed algorithm. It is validated both in
synthetic experiments and on publicly available real-world datasets that the
method always improves the output of the evaluated affine-covariant feature
detectors. As a by-product, these detectors are compared and the ones obtaining
the most accurate affine frames are reported. For demonstrating the
applicability, we show that the proposed technique as a pre-processing step
improves the accuracy of pose estimation for a camera rig, surface normal and
homography estimation.
