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Retinal image registration, which can be formulated by matching two sets of sparse features extracted from two observed retinal images, is a crucial step in the diagnosis and treatment of various eye diseases. Existing methods suffer from missing true correspondences or do not fully consider local appearance information, which causes difficulty in matching low-quality retinal images due to insufficient...
In this paper, we propose a method for multimodal retinal image registration based on feature guided Gaussian mixture model (GMM) and edge map. We extract two sets of feature points from the edge maps of two images, and formulate image registration as the estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian...
In this paper, we propose a novel feature guided Gaussian mixture model (FG-GMM) for image matching, which typically requires matching two sets of feature points extracted from the given images. We formulate the problem as estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to...
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