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Binary descriptors are becoming widely used in computer vision field because of their high matching efficiency and low memory requirements. Since conventional approaches, which first compute a floating-point descriptor then binarize it, are computationally expensive, some recent efforts have focused on directly computing binary descriptors from local image(More)
Feature point matching is essential in computer vision. In this paper, we propose a robust feature point matching framework in which we first obtain a set of refined matches from ranked initial-matches based on a restricted affine invariant spatial constraint, and then compute a global geometrical transformation from the refined matches. After that, we(More)
Repetitive patterns exist widely in real world images, and matching images with plenty of repetitive patterns remains a challenging task. We present in this paper a novel feature matching algorithm of images with notably repetitive patterns, in which a reliable initial correspondence set is established, purified and propagated using a voting strategy,(More)
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