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I read about SIFT, SURF, Fern, BRIFT and even the evolution algorithms. But I not sure which from those algorithms is the best. So I need your help. Of course I know each algorithms have its own advantages, so here is the key for you to classify:

  • Which is fastest in training/ recognize phases?
  • Which consume fewest memory in runtime?
  • Which can be implement for detect 3D object?

Thank you, and sorry about my bad English. In my case, I want to implement an application on smartphone to recognize a known object.

Tai Le
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1 Answers1

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Your question is a bit complicated.

There are no optimal methods for all cases but methods that suit certain very specific cases.

If you decide to use local descriptors in your method I advise you to get started by using SIFT / SURF which are the most popular descriptors but are not very efficient (slow) and require a lot of memory. After that, you can try to replace them with binary descriptors (eg. BRIEF, ORB, BRISK, FREAK) that are much more efficient and require less storage.

But as I said before it all depends on what you want to implement, and what are the requirements of your application.

zedv
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  • I will read about BRISK and FREAK. The two is new with me. Thanks for mention about them. – Tai Le Dec 08 '14 at 06:49
  • BRISK: http://www.robots.ox.ac.uk/~vgg/rg/papers/brisk.pdf FREAK:http://infoscience.epfl.ch/record/175537/files/2069.pdf – zedv Dec 08 '14 at 12:56