Matching purified based on singular value decomposition

Generally, purified algorithms of image matching points use some of the points as initial input. For the algorithms, as the purification results are quite easy to fall into a local optimum, they usually have such problems as rejecting some of correct matching points. To solve this problem, we introduce singular value decomposition model, which take the whole matches as input to obtain a more accurate result through iterative robust solving. Extensive experiments on practical images demonstrate the excellent performance of our proposed method.