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I am doing some research in local feature representation, so sift, surf and such Fast, harris or censure and then use the sift descriptor on the reflected keypoint Now, has anybody here ever tried brief and orb
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If so, can you discuss what are some of the pro and con with respe. Use a symmetric keypoint detector, e.g I tested orb, sift and surf matchings
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Sift is the best followed by surf, then orb is the last
But people say orb is better than sift In my case i wonder why Original images are also attached. Surf is patented, as is sift
Are there any feature extractors. Surf is fundamentally faster, by a larger amount, than sift if you were to count flops of two well written implementations Sift computes an image pyramid by convolving the image several times with large gaussian kernels, while surf accomplishes an approximation of that using integral images. When you run sift on an image of some object (e.g
A car), sift will try to create the same descriptor for the same feature (e.g
The license plate), no matter what image transformation you apply So orb and sift try to match features in a pair of images The reason why you have mismatching is because some of the features are too similar and the system mistakes them as a match You will need to increase your detector's threshold the matcher's acceptable matches.
Therefore, the orb feature detection method is more computationally efficient than sift and surf methods. Orb, brief, brisk, freak, akaze, etc Orb is basically an evolution of the previous 2 detectors (orb stands for oriented fast and rotated brief) that is rotation invariant and also implements its own descriptor, this is probably the best choice for general purposes, it is free to use and its robustness is comparable to sift/surf/akaze while the performances slightly overcomes them. If you're interested in matching reflected images, you'll need to do one of the following
