DocumentCode :
1376221
Title :
BRIEF: Computing a Local Binary Descriptor Very Fast
Author :
Calonder, M. ; Lepetit, Vincent ; Ozuysal, Mustafa ; Trzcinski, Tomasz ; Strecha, Christoph ; Fua, Pascal
Author_Institution :
Comput. Vision Lab., Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland
Volume :
34
Issue :
7
fYear :
2012
fDate :
7/1/2012 12:00:00 AM
Firstpage :
1281
Lastpage :
1298
Abstract :
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.
Keywords :
feature extraction; image matching; BRIEF; feature points; floating point; intensity difference tests; local binary descriptor; recognition accuracy; Accuracy; Databases; Hamming distance; Principal component analysis; Quantization; Real time systems; Vectors; Image processing and computer vision; augmented reality; feature matching; real-time matching.;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2011.222
Filename :
6081878
Link To Document :
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