Corner Matching Refinement for Monocular Pose Estimation

Many tasks in computer vision rely on accurate detection and matching of visual landmarks (e.g. image corners) between two images. In particular, for the calculation of epipolar geometry from a minimal set of five correspondences the spatial accuracy of matched landmarks is critical because the result is very sensitive to errors. The most common way of improving the accuracy is to calculate a sub-pixel location independently for each landmark in the hope that this reduces the re-projection error of the point in space to which they refer. This paper presents a method for refining the coordinates of correspondences directly. Thus given some coordinates in the first image, our goal is to maximise the accuracy of the estimate of the coordinates in second image corresponding to the same real world point without being too concerned about which real world point is being matched. We show how this can be achieved as a frequency domain optimisation between two image patches to refine the correspondence by estimating affine parameters. We select the correct frequency range for optimisation by identifying a direct relationship between the Gabor phase based approach and the frequency response of a patch. Further, we show how parametric estimation can be made accurate by operating in the frequency domain. Finally, we present experiments which demonstrate the accuracy of this approach, its robustness to changes in scale and orientation and its superior performance by comparison to other sub-pixel methods.