Abstract:

Aiming at the limitation of spectral matching by relying solely on singular value decomposition, a Laplace spectral matching algorithm combined with geodesic-intensity histogram (GIH) for point pattern matching is described. Firstly, the Laplace matrices are obtained from the point sets of the images. By using the eigenvectors of the matrices, the initial matching probabilities are computed. Then, the GIH with local similarity is introduced as a compatibility constraint. And the matching probabilities are refined via the iterative relaxation approach. Experimental results show the algorithm gets the complementation of multifeature and multi-algorithm, and improves the matching precision and the application of the spectral matching method.