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Introduction. Recently, a comparative study in [2] has shown the superior performance of local features for face recognition in unconstrained environments. Due to the global integration of Speeded Up Robust Features (SURF) [1], the authors claim that it stays more robust to various image perturbations than the more locally operating SIFT descriptor. However(More)
In this paper, we propose a novel algorithm for general 2D image matching, which is known to be an NP-complete optimization problem. With our algorithm, the complexity is handled by sequentially optimizing the image columns from left to right in a two-level dynamic programming procedure. On a local level, a set of hypotheses is computed for each column,(More)
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