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Hi,

We experimented various matchers with SURF.

FLANN is fast but... gives low performances in difficult context (heterogeneous/various) dataset.

Brute force matchers like L1 or L2 based distance give good results.

If you consider L2 based bruteforce matchers, consider L2 distance without the square root computation which does not introdue error in this matching case and allows less processing to be performed.

Typical use :

//Allocate your image descriptor and your matcher with a OpenCV pointer (do not care about the object delete step): //-> descriptor:

cv::Ptr<cv::descriptorextractor> _descExtractor = DescriptorExtractor_Custom::create("SURF"); /*-> matcher: define a string keyword that shows which matcher to choose :

*BruteForce (it uses real L2 )

*BruteForce-SL2 (not in the documentation, BUT this is the one that skeeps the squared root !)

*BruteForce-L1

*BruteForce-Hamming

*BruteForce-Hamming(2)

*FlannBased

*/ cv::Ptr<cv::descriptormatcher> _descMatcher = cv::DescriptorMatcher::create(keyword );

Have a nice coding ;o)

Hi,

We experimented various matchers with SURF.

FLANN is fast but... gives low performances in difficult context (heterogeneous/various) dataset.

Brute force matchers like L1 or L2 based distance give good results.

If you consider L2 based bruteforce matchers, consider L2 distance without the square root computation which does not introdue error in this matching case and allows less processing to be performed.

Typical use :

//Allocate your image descriptor and your matcher with a OpenCV pointer (do not care about the object delete step): step):

//-> 1. descriptor:

cv::Ptr<cv::descriptorextractor> _descExtractor = DescriptorExtractor_Custom::create("SURF"); DescriptorExtractor_Custom::create("SURF");

/*-> 2. matcher: define a string keyword that shows which matcher to choose :

*BruteForce (it uses real L2 )

*BruteForce-SL2 (not in the documentation, BUT this is the one that skeeps the squared root !)

*BruteForce-L1

*BruteForce-Hamming

*BruteForce-Hamming(2)

*FlannBased

*/ cv::Ptr<cv::descriptormatcher> _descMatcher = cv::DescriptorMatcher::create(keyword );

Have a nice coding ;o)

Hi,

We experimented various matchers with SURF.

FLANN is fast but... gives low performances in difficult context (heterogeneous/various) dataset.

Brute force matchers like L1 or L2 based distance give good results.

If you consider L2 based bruteforce matchers, consider L2 distance without the square root computation which does not introdue error in this matching case and allows less processing to be performed.

Typical use :

//Allocate your image descriptor and your matcher with a OpenCV pointer (do not care about the object delete step):

//-> 1. descriptor:

cv::Ptr<cv::descriptorextractor> _descExtractor = DescriptorExtractor_Custom::create("SURF");

/*-> 2. matcher: define a string keyword that shows which matcher to choose :

*BruteForce (it uses real L2 )

*BruteForce-SL2 (not in the documentation, BUT this is the one that skeeps the squared root !)

*BruteForce-L1

*BruteForce-Hamming

*BruteForce-Hamming(2)

*FlannBased

*/ cv::Ptr<cv::descriptormatcher> _descMatcher = cv::DescriptorMatcher::create(keyword );

Finally, regarding good matches sorting, you should take a look at the RANSAC method that allows global a displacement identification and not corresponding matches pop out. Have a nice coding ;o)

Hi,

We experimented various matchers with SURF.

FLANN is fast but... gives low performances in difficult context (heterogeneous/various) dataset.

Brute force matchers like L1 or L2 based distance give good results.

If you consider L2 based bruteforce matchers, consider L2 distance without the square root computation which does not introdue error in this matching case and allows less processing to be performed.

Typical use :

//Allocate your image descriptor and your matcher with a OpenCV pointer (do not care about the object delete step):

//-> 1. descriptor:

cv::Ptr<cv::DescriptorExtractor> _descExtractor = DescriptorExtractor_Custom::create("SURF");
DescriptorExtractor_Custom::create("SURF");

cv::Ptr<cv::DescriptorMatcher> _descMatcher = cv::DescriptorMatcher::create(keyword );
);