Feature-matching methods for attempting to match visual features in one image with visual features in another image. Feature-matching methods disclosed progressively sample the affine spaces of the images for visual features, starting with a course sampling and iteratively increasing the density of sampling. Once a predetermined threshold number of unambiguous matches has been satisfied, the iterative sampling and matching can be stopped. The iterative sampling and matching methodology is especially, but not exclusively, suited for use in fully affine invariant feature matching applicants and can be particularly computationally efficient for comparing images that have large differences in observational parameters, such as scale, tilt, object-plane rotation, and image-plane rotation. The feature-matching methods disclosed can be useful in object/scene recognition applications. The disclosed methods can be implemented in software and various object/scene recognition systems.
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