2 research outputs found
Homography from two orientation- and scale-covariant features
This paper proposes a geometric interpretation of the angles and scales which
the orientation- and scale-covariant feature detectors, e.g. SIFT, provide. Two
new general constraints are derived on the scales and rotations which can be
used in any geometric model estimation tasks. Using these formulas, two new
constraints on homography estimation are introduced. Exploiting the derived
equations, a solver for estimating the homography from the minimal number of
two correspondences is proposed. Also, it is shown how the normalization of the
point correspondences affects the rotation and scale parameters, thus achieving
numerically stable results. Due to requiring merely two feature pairs, robust
estimators, e.g. RANSAC, do significantly fewer iterations than by using the
four-point algorithm. When using covariant features, e.g. SIFT, the information
about the scale and orientation is given at no cost. The proposed homography
estimation method is tested in a synthetic environment and on publicly
available real-world datasets