Geometrical Consistency Voting Strategy for Outlier Detection in Image Matching
Luping Lu, Yong Zhang, Pengjie Tao · Photogrammetric Engineering & Remote Sensing · 2016
False matches in tie-point image matching are common. This paper introduces a straightforward and effective prepossessing method to reject false matches from initial matches. The method is based on the idea of Hough transform using only two geometrical consistency parameters, namely, the scale parameter and the rotation parameter between two images. A weighted voting strategy is employed, and it can further improve the robustness of the algorithm. The method can handle a large rate of outliers and produce more robust matches with low complexity. No assumptions with regard to the relative pose between two images are necessary, and large perspective deformation can be handled as well. Experiments with ground reference data show that the algorithm works effectively even when the ratio of inliers is below 10 percent. In these data, the ratio of inliers can be improved from 5 percent to 40 percent on average.