Maximally Stable Extremal Regions - Robust Wide-baseline Algorithm

Robust Wide-baseline Algorithm

The purpose of this algorithm is to match MSERs to establish correspondence points between images. First MSER regions are computed on the intensity image (MSER+) and on the inverted image (MSER-). Measurement regions are selected at multiple scales: the size of the actual region, 1.5x, 2x, and 3x scaled convex hull of the region. Matching is accomplished in a robust manner, so it is better to increase the distinctiveness of large regions without being severely affected by clutter or non-planarity of the region's pre-image. A measurement taken from an almost planar patch of the scene with stable invariant description are called a 'good measurement'. Unstable ones or those on non-planar surfaces or discontinuities are called 'corrupted measurements'. The robust similarity is computed: For each on region regions from the other image with the corresponding i-th measurement nearest to are found and a vote is cast suggesting correspondence of A and each of . Votes are summed over all measurements, and using probability analysis, we pick out the 'good measurements' as the 'corrupt measurements' will likely spread their votes randomly. By applying to the centers of gravity of the regions, we can compute a rough epipolar geometry. An affine transformation between pairs of potentially corresponding regions is computed, and correspondences define it up to a rotation, which is then determined by epipolar lines. The regions are then filtered, and the ones with correlation of their transformed images above a threshold are chosen. is applied again with a more narrow threshold, and the final eipolar geometry is estimated by the eight-point algorithm.

This algorithm can be tested here (Epipolar or homography geometry constrained matches): WBS Image Matcher