BRIEF: Computing a Local Binary Descriptor very Fast.

IEEE Transactions on Pattern Analysis and Machine Intelligence(2012)

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摘要
Binary descriptors are becoming increasingly popular as a means to compare feature points very fast and while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor we call BRIEF on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.
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关键词
feature point,simple intensity difference test,local binary descriptor,comparable recognition accuracy,small amount,binary descriptors,binary descriptor,standard benchmarks,typical approach,vectors,databases,accuracy,floating point,surf,real time systems,augmented reality,computer vision,feature extraction,real time,sift,principal component analysis,quantization,hamming distance,point matching
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