A method is proposed for correcting the parameters of a sequence of detected local affine frames through multiple views. 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 upgraded 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 almost 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. To demonstrate the applicability in real-world scenarios, we show that the proposed technique improves the accuracy of pose estimation for a camera rig, surface normal and homography estimation.
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