Turnover and shape filter based feature matching for image stitching

This work intends to deal with the problem of misalignment in image stitching caused by small overlap area. To reduce mismatches between matched features pairs in two connected images, random sample consensus (RANSAC) [1] is usually adopted, which works under the assumption that the sampling of matched feature points with the largest number of inliers should be utilized to compute geometric matrix. However, this assumption does not hold in the case of small overlap area between the connected images, as compressing or turning over the image may result in better spatial consistency of matched feature points. Therefore, we propose a turnover and shape filter based feature matching method for image stitching. In the method, a turnover and shape filter is firstly used to filter out the samplings resulted from turnover and compression, which is then connected to RANSAC to yield final inliers. Experimental results from real-world datasets validate the effectiveness of our method.