Feature matching of images without corners I am trying to stitch aerial images using feature-based registration. Most image pairs are matched well, such as this:



Others are not matched due to lack of common features:



As you can see, the images contain almost no corners (only fuzzy edges).

Is there a way to register such images? Please note that I need homography so template matching is not possible. The images are taken by flying drone wiggling in the air, hence the images have perspective distortions and feature matching is therefore essential.

Please note I have tried many point features (Harris, SIFT, SURF, MOPS) with similar (bad) results.

My ideas:


Use some line-based (Hough transform?) or area-based features (MSER?)
Use more images or video and then recover homography from dense data using Horn-Schunck or Lucas-Kanade


Here you can download the source images - it contains two pairs of images, the easier pair and hard-to-align pair:

Download original images
I have referred to Stage I.D of this tutorial. Hope this helps.
http://www.robots.ox.ac.uk/~vgg/practicals/instance-recognition/index.html#stage-id-improving-sift-matching-using-a-geometric-transformation

When the features have scale and orientation assigned (e.g. SIFT features have these properties), you can compute similarity transform between each corresponding feature pair. The similarity transform has 4 parameters: Tx, Ty - translation, R - rotation and S - scale.

The tentative correspondences are then filtered using RANSAC-like algorithm such that only correspondences consistent with common similarity transform (up to a predefined threshold) are used.