Application of clustering analysis and dynamic programming in the image mosaic

In the process of image mosaic, the traditional image match optimization algorithm is low efficiency, and ghosting artifact is often contained in the merged image. In order to solve these problems, this paper presents that cluttering algorithm is used for different scales of feature points. The parameters of global affine transformation are got by the clustering algorithm, which is used to filter the matched point, so it can eliminate the false matching points. We propose a dynamic programming (DP) method to find the best stitching line. The weighted average method is used to achieve smooth stitching results and eliminate intensity seam effectively. The experimental results show that the proposed algorithm has good result of removing false matching and ghosting artifacts, it is robust.