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Establishing reliable correspondences is often challenging for retinal image registration with poor quality and low overlap. The conventional local search methods usually find many incorrect correspondences by feature descriptor, which would degrade the accuracy of image registration. In this paper, we propose a robust correspondence detection framework for low overlap and poor quality retinal image...
Accurate retinal Image registration is essential to monitor and track the progress of various diseases. Since it is low quality images on Non-mydriatic or with the disease. Then vascular structure will become less clear. It becomes more difficult for the general registration methods. In this paper, a novel feature based retinal image registration method is proposed to solve this problem. SIFT(Scale...
It is important to determine the stable keypoints and select transformation models for image registration and mosaic. In this paper a method is presented for retinal image mosaic. Central to the new method is to detect the PCA-SIFT (principal components analysis-scale invariant feature transform) feature and estimate the quadratic transformation model which is employed to simulate the anatomy of human...
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