What is it about?
It presents a new computational framework that uses multimodal ophthalmic images, transforms them through a sequence of image processing and computational techniques with pairwise operations, and outputs registered images. In contrast to the traditional spatial domain techniques, which in general rely on key points detection and the bifurcation properties of blood vessels, this approach considers the transformation of the image energy over a series of connected and parametrized feature spaces.
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This page is a summary of: Laplacian feature detection and feature alignment for multimodal ophthalmic image registration using phase correlation and Hessian affine feature space, Signal Processing, December 2020, Elsevier,
DOI: 10.1016/j.sigpro.2020.107733.
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