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Robust point pattern matching based on spectral context
Pattern Recognition
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This paper aims to solve the problem of matching images containing repetitive patterns. Although repetitive patterns widely exist in real world images, these images are difficult to be matched due to local ambiguities even if the viewpoint changes are not very large. It is still an open and challenging problem. To solve the problem, this paper proposes to match pairs of interest points and then obtain point correspondences from the matched pairs of interest points based on the low distortion constraint, which is meant that the distortions of point groups should be small across images. By matching pairs of interest points, local ambiguities induced by repetitive patterns can be reduced to some extent since information in a much larger region is used. Meanwhile, owing to our newly defined compatibility measure between one correspondence and a set of point correspondences, the obtained point correspondences are very reliable. Experiments have demonstrated the effectiveness of our method and its superiority to the existing methods.