Maximal Self-Dissimilarity Interest Point Detector
	Computer Vision Laboratory, University of Bologna
		   www.vision.deis.unibo.it/fede

This is an implementation of the work presented in the paper

[1] F. Tombari, L. Di Stefano 
“Interest Points via Maximal Self-Dissimilarities”
12th Asian Conference on Computer Vision (ACCV), 2014

We kindly request to cite and adequately refer to this work any time this code is 
being used. 

In the current distribution you can find 
  -an implementation of the MSD descriptor 
  -a test that demonstrates how to use it on a test image

The current implementation of MSD extracts interest point out of grayscale images 
at different scales. The input can be either a grayscale of a color image. The 
detector returns a std::vector of cv::KeyPoint, an OpenCV structure which 
associates each interest point to a 2D coordinate, a scale, a saliency value and 
(optionally) a canonical orientation. The MSD algorithm can be used with the set
of default parameters (those used in [1]), as well as with a set of user-defined
parameters. 

The MSD code is released under the GNU GPLv3 license. A version of this code under 
a different licensing agreement, intended for commercial use, is also available. 
Please contact me if interested.

DEPENDENCIES and BUILD:
The only required dependency is OpenCV (opencv.org, v3.0 or above). Boost (v1.49 
or above, www.boost.org) is not required, but used for multi-thread optimization. 
It is expected that the running time of MSD can be sped up notably on a multi-core 
architecture by using the provided Boost multi-thread optimization. 

We provide a CMakeFile, so that the code can be build over different compilers 
and platforms by making use of CMake (www.cmake.org).

CONTACTS:
  Federico Tombari <[email protected]>