@article{
 title = {BRIEF: Binary robust independent elementary features},
 type = {article},
 year = {2010},
 pages = {778-792},
 volume = {6314 LNCS},
 id = {2d6d377b-a489-36a5-8beb-10f8f7a45e32},
 created = {2022-09-19T10:49:11.123Z},
 file_attached = {true},
 profile_id = {276016a7-2c9d-3507-8888-093db7c54774},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2022-09-19T10:49:45.078Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {02fb5526-03ff-44ad-8d5c-42bd496c3100},
 private_publication = {false},
 abstract = {We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF.We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using the Hamming distance, which is very efficient to compute, instead of the L 2 norm as is usually done. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and U-SURF on standard benchmarks and show that it yields a similar or better recognition performance, while running in a fraction of the time required by either. © 2010 Springer-Verlag.},
 bibtype = {article},
 author = {Calonder, Michael and Lepetit, Vincent and Strecha, Christoph and Fua, Pascal},
 doi = {10.1007/978-3-642-15561-1_56},
 journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 number = {PART 4}
}

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