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                           Boosted Descriptors
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This software is an implementation of the boosted descriptors presented in the following papers:

[1] T. Trzcinski, M. Christoudias, P. Fua, V. Lepetit. Boosting Binary Keypoint Descriptors. Computer Vision and Pattern Recognition (CVPR) 2013.
[2] T. Trzcinski, M. Christoudias, V. Lepetit, P. Fua. Learning Image Descriptors with the Boosting-Trick. Neural Information Processing Systems (NIPS) 2012. 

The code is released under the GNU General Public License V2 (attached within this package).

COMPILATION:

Type

$ make

to  compile  the  code  and   generate  the  executable 'DBRIEF_demo'.

The code depends  on OpenCV-2.4 or later, so you should  first install 
and make sure that pkg-config can locate it.  To test it, type

$ pkg-config  opencv --libs  --cflags

and you should get a list of compiler flags.

Important Notices:

1) This demo relies on  Hamming Distance  calculation.  Some  of new
generation  processors support SSE4.2  instructions which  includes an
instruction  called   POPCNT  which  enables   fast  Hamming  Distance
calculation.   In g++  SSE4.2 instructions  are enabled  with -msse4.2
flag. By  default, this  flag is set  in the Makefile.   Therefore, if
your instruction  set does  not support SSE4.2  instructions, although
this code compiles perfectly, it  will crash giving an error message.
If  you get such  an error,  please open  Makefile with  your favorite
editor and remove -msse4.2 flag.

USAGE:

./main (--extract <imgFile> <outputFile> <descriptor>
          	| --match <imgFile1> <imgFile2> <descriptor>)

Descriptor names: BGM, LBGM, BINBOOST(-64/128/256)

Example usage:
$ ./main --match graf/img1.ppm graf/img2.ppm binboost-256

Image of matched images should be saved to 'matches.png'.

FOR REPORTING BUGS and FEEDBACK:
Please send an email to Tomasz Trzcinski: [email protected] 
and/or Mario Christoudias: [email protected] 
and/or Vincent Lepetit: [email protected].