| We present a new point set registration method with global-local correspondence and transformation estimation(GL-CATE).The geometric structures of point sets are exploited by combining the global feature,the point-to-point Euclidean distance,with the local feature,the shape distance(SD)which is based on the histograms generated by an elliptical Gaussian soft count strategy.By using a bi-directional deterministic annealing scheme to directly control the searching ranges of the two features,the mixture-feature Gaussian mixture model(MGMM)is constructed to recover the correspondences of point sets.A new vector-based structure constraint term is formulated to regularize the transformation.The accuracy of transformation updating is improved by constraining spatial structure at both global and local scales.Both processes are incorporated in the EM algorithm,a unified optimization framework.We test the performances of our GL-CATE in contour registration,sequence images,real images,medical images,fingerprint images and remote sensing images,and compare with eight state-of-the-art methods where our GL-CATE shows favorable performances in most scenarios.Low-altitude aerial photography using small unmanned aerial vehicles(SUAVs)with large viewpoint changes causes low overlap ratios and non-rigid distortions.We also present a non-rigid feature-based low-altitude SUAV image-registration method.The key idea of our method is to maintain a high matching ratio on inliers while taking advantage of outliers for varying the warping grids.Thus,accurate image transformation over the overlapping areas as well as a good approximation of the real transformation over the non-overlapping areas can be obtained.Experiments on feature matching and image registration are performed using 42 pairs of SUAV images.Our method exhibited a favorable performance as compared with four state-of-the-art methods,even with up to 80% outliers. |
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