Abstract:

This paper proposed a quick, affine invariance matching method for oblique images. It calculated the initial affine matrix by making full use of the two estimated camera axis orientation parameters of an oblique image, then recovered the oblique image to a rectified image by doing the inverse affine transform, and left over by the SIFT method. We used the nearest neighbor distance ratio(NNDR), normalized cross correlation(NCC) measure constraints and consistency check to get the coarse matches, then used RANSAC method to calculate the fundamental matrix and the homography matrix. And we got the matches that they were interior points when calculating the homography matrix, then calculated the average value of the matches' principal direction differences. During the matching process, we got the initial matching features by the nearest neighbor(NN) matching strategy, then used the epipolar constrains, homography constrains, NCC measure constrains and consistency check of the initial matches' principal direction differences with the calculated average value of the interior matches' principal direction differences to eliminate false matches. Experiments conducted on three pairs of typical oblique images demonstrate that our method takes about the same time as SIFT to match a pair of oblique images with a plenty of corresponding points distributed evenly and an extremely low mismatching rate.

Key words: oblique images matching, affine invariant feature(AIF), ASIFT, inverse affine transform, matching constrains