SURF is patented, as is SIFT. ORB and BRIEF are not patented, but their features are not scale-invariant, seriously limiting their usefulness in complex scenarios.
Are there any feature extractors that can extract scale-invariant features as fast as SURF and are not so strictly patented as SURF and SIFT?
Although you already choose BRISK, you might find FREAK interesting. Author claims to have better results than BRISK and ORB. I should also add that ORB is scale-invariant but has some problems in that area. So I would still recommend it for someone to try it.
The FREAK source code is compatible with OpenCV (how easy it is to merge them I do not know) and the author is working on submitting it to the OpenCV project.
EDIT:
FREAK is now part of opencv feature detectors / descriptors.
You can read here more about the differences between several feature detectors/extractors, and also a series of benchmarks which includes FREAK and other popular ones.
FREAK is supposed to be the fastest scale and rotation invariant descriptor extractor, it is open source and you can use it easily as it is implemented in OpenCV. You need a binary matcher that uses the Hamming Distance, like the BruteForceMatcher.
Here you have an example on how to use it, easily replaceble by SIFT descriptor.
I ended up using Brisk, which is a feature detector with performance comparable to SURF but under the BSD licence. Plus, it's got a very nice open source C++ implementation that plugs in easily to the OpenCV FeatureDetector framework, so it's like 2 lines choosing to use Brisk over SURF in your code.
You might try multi-scale histogram of oriented gradients. It won't be fully scale-invariant, but if your data are constrained with a reasonable set of scale limits (often the case in practice) then this can probably work for you.
Another approach, depending totally on what your intended application is, would be to leverage poselets, even if they are built on top of a non-scale-invariant descriptor like plain histogram of oriented gradient, or appearance models. If the annotations in your training data include examples of different items for detection all at different scales, then the Procrustes-style distance used in Poselets for training should take care of a lot of scale-invariance. This may not be satisfactory though if your primary application is not localized detection of parts.
As an aside, I think it's rather unfortunate that SIFT and SURF were capable of being patented this way, given that they were (at least in part) funded with tax-payer dollars through grants.
You can try KAZE, it is supposed to be faster and more precise (the videos seem fancy, but I did not get to try it myself yet). There is also an accelerated version of it available.
Pablo F. Alcantarilla, Adrien Bartoli and Andrew J. Davison, "KAZE Features", In European Conference on Computer Vision (ECCV). Fiorenze*, Italy. October 2012.
Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli, "Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces", In British Machine Vision Conference (BMVC). Bristol, UK. September 2013
The source codes, videos and more can be found at the author's website.
*Firenze
