title: Suggestion for object recognition algorithm using depth information
body: <p>I have recently implemented a recognition software following the technique described in <a href="http://saturne.xrce.xerox.com/layout/set/print/content/download/20785/148346/file/2004_010.pdf" rel="nofollow">this paper</a>. However, my dataset also contains depth maps taken with OpenNI.</p>
  
  <p>I'd like to increase robustness of the recognizer using depth information. I though about training 1-vs-all SVMs computing bow response histograms after extracting VFH descriptors (I adapted OpenCV DescriptorExtractor interface for this task). But the point is: how can I combine the two things to get more precise results? Can someone suggest me a strategy for this?</p>
  
  <p>P.s. I would very much like to test the recognizer directly showing objects to a kinect (and not, like I'm doing right now, feeding cropped images to the recognizer).   </p>