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Green Image
Issue No. 07 - July (2012 vol. 34)
ISSN: 0162-8828
pp: 1281-1298
Michael Calonder , EPFL, Lausanne
Vincent Lepetit , EPFL, Lausanne
Mustafa Özuysal , EPFL, Lausanne
Tomasz Trzcinski , EPFL, Lausanne
Christoph Strecha , EPFL, Lausanne
Pascal Fua , EPFL, Lausanne
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.
Image processing and computer vision, feature matching, augmented reality, real-time matching.

C. Strecha, V. Lepetit, T. Trzcinski, M. Calonder, M. Özuysal and P. Fua, "BRIEF: Computing a Local Binary Descriptor Very Fast," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1281-1298, 2011.
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