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I am doing some research in Local Feature representation, so SIFT, SURF and such.
Now, has anybody here ever tried BRIEF and ORB? If so, can you discuss what are some of the pro and con with respective to SIFT?
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We showed that ORB is the fastest algorithm while SIFT performs the best in the most scenarios. For special case when the angle of rotation is proportional to 90 degrees, ORB and SURF outperforms SIFT and in the noisy images, ORB and SIFT show almost similar performances.
After comparing SIFT, SURF and ORB, we can notice ORB is the fast algorithm. From the result, we can assume ORB gets keypoint more efficient than others.
SURF is better than SIFT in rotation invariant, blur and warp transform. SIFT is better than SURF in different scale images. SURF is 3 times faster than SIFT because using of integral image and box filter. SIFT and SURF are good in illumination changes images.
ORB uses BRIEF descriptors but as the BRIEF performs poorly with rotation. So what ORB does is to rotate the BRIEF according to the orientation of keypoints. Using the orientation of the patch, its rotation matrix is found and rotates the BRIEF to get the rotated version.
here is one comparison I have found helpful. Essentially BRIEF and ORB are much faster. There is not a good comparison of scale invariance there but personally I have found SURF/SIFT to be more scale invariant than BRIEF and ORB. I recommend if you are going to use these for a specific use case you try both to see which meets your needs best.
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SURF/SIFT uses patents which need to be payed somehow. I'm not up to date on this but the costs can be significant. So i would go with ORB if possible - except of course if you don't care about money :)
SIFT: The algorithm is patented in the US; the owner is the University of British Columbia. (http://en.wikipedia.org/wiki/Scale-invariant_feature_transform)
SURF: An application of the algorithm is patented in the US. (http://en.wikipedia.org/wiki/SURF)
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