SIFT, HOG and SURF c++, opencv

I have a simple question, which I want to know, what kind of libraries are available and can give good results for implementing SIFT, HOG(Histogra开发者_Go百科m Oriented Gradient) and SURF in c++ or opencv?

Hence: 1- Give me the link for the code if you can, which I will be so appreciated. 2- If you know one of them or any kind of information to lead me to what I want, I will be so appreciated as well.

Thanks

check these: surf - great article http://people.csail.mit.edu/kapu/papers/mar_mir08.pdf sift - great source, I tried it on the iPhone http://blogs.oregonstate.edu/hess/ - fast - fast corner detection library http://svr-www.eng.cam.ac.uk/~er258/work/fast.html

Example of surf code in openCV https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/cpp/matching_to_many_images.cpp

Not sure if this is still relevant, but you also get two implementations of computing HOG descriptors in opencv i.e. both GPU and CPU versions of the HOG code.

for the CPU version you can check this blog post

however in the CPU version you would need to write your own logic for sliding windows.

and the GPU version is fairly straightforward you can read the documentation here

Might help you to know that SIFT and SURF implementations are already integrated into OpenCV. http://opencv.willowgarage.com/documentation/cpp/features2d__feature_detection_and_descriptor_extraction.html

Be careful about OpenCV implementations, because latest versions of OpenCV have classified SIFT and SURF implementations as nonfree http://docs.opencv.org/modules/nonfree/doc/nonfree.html.

Now you can use them, but probably they are subject to licensing and cannot be used for commercial solutions.

This one uses descriptors based on HoG, Sobel and Lab channels for detection Class-Specific Hough Forests for Object Detection (opencv/c source code).

Rather then performing detection at every possible location this approach calculates a vote for each descriptor, then when putted together they produce a voting cloud where maximum will correspond to most probable location of the target. When combined with cvGoodFeaturesToTrack can produce very good results, even with a small training database.