Abstract
Finding feature correspondences between a pair of images is a fundamental problem in computer vision for 3D reconstruction and target recognition. In practice, for feature based matching methods, there is often having a higher percentage of incorrect matches and decreasing the matching accuracy, which is not suitable for subsequent processing. In this paper, we develop a novel algorithm to find good and more correspondences. Firstly, detecting SURF keypoints and extracting SURF descriptors; Then Obtain the initial matches based on the Euclidean distance of SURF descriptors; Thirdly, remove false matches by sparse representation theory, at the same time, exploiting the information of SURF keypoints, such as scale and orientation, forming the geometrical constraints to further delete incorrect matches; Finally, adopt Delaunay triangulation to refine the matches and get the final matches. Experimental results on real-world image matching datasets demonstrate the effectiveness and robustness of our proposed method.
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