BRIEF: Computing a Local Binary Descriptor Very Fast

被引:547
作者
Calonder, Michael [1 ]
Lepetit, Vincent [1 ]
Oezuysal, Mustafa [1 ]
Trzcinski, Tomasz [1 ]
Strecha, Christoph [1 ]
Fua, Pascal [1 ]
机构
[1] Ecole Polytech Fed Lausanne, Comp Vis Lab, I&C Fac, CH-1015 Lausanne, Switzerland
基金
瑞士国家科学基金会;
关键词
Image processing and computer vision; feature matching; augmented reality; real-time matching;
D O I
10.1109/TPAMI.2011.222
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Binary descriptors are becoming increasingly popular as a means to compare feature points very fast 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, which 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.
引用
收藏
页码:1281 / 1298
页数:18
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