title: GPU based SIFT feature extractor for iOS?
body: <p>I've been playing with the excellent GPUImage library, which implements several feature detectors: Harris, FAST, ShiTomas, Noble. However none of those implementations help with the feature extraction and matching part. They simply output a set of detected corner points.</p>
  
  <p>My understanding (which is shakey) is that the next step would be to examine each of those detected corner points and extract the feature from then, which would result in descriptor - ie, a 32 or 64 bit number that could be used to index the point near to other, similar points. </p>
  
  <p>From reading Chapter 4.1 of [Computer Vision Algorithms and Applications, Szeliski], I understand that using a BestBin approach would help to efficient find neighbouring feautures to match, etc. However, I don't actually know how to do this and I'm looking for some example code that does this. </p>
  
  <p>I've found this project [https://github.com/Moodstocks/sift-gpu-iphone] which claims to implement as much as possible of the feature extraction in the GPU. I've also seen some discussion that indicates it might generate buggy descriptors. </p>
  
  <p>And in any case, that code doesn't go on to show how the extracted features would be best matched against another image. </p>
  
  <p>My use case if trying to find objects in an image. </p>
  
  <p>Does anyone have any code that does this, or at least a good implementation that shows how the extracted features are matched? I'm hoping not to have to rewrite the whole set of algorithms.</p>
  
  <p>thanks, 
  Rob. </p>