title: Classification of detectors, extractors and matchers
body: <p>I am new to opencv and trying to implement image matching between two images. For this purpose, I'm trying to understand the difference between feature descriptors, descriptor extractors and descriptor matchers. I came across a lot of terms and tried to read about them on the opencv documentation website but I just can't seem to wrap my head around the concepts. I understood the basic difference here. <a href="https://stackoverflow.com/questions/6832933/difference-between-feature-detection-and-descriptor-extraction?rq=1">Difference between Feature Detection and Descriptor Extraction</a></p>
  
  <p>But I came across the following terms while studying on the topic : </p>
  
  <blockquote>
    <p>FAST, GFTT, SIFT, SURF, MSER, STAR, ORB, BRISK, FREAK, BRIEF </p>
  </blockquote>
  
  <p>I understand how FAST, SIFT, SURF work but can't seem to figure out which ones of the above are only detectors and which are extractors.</p>
  
  <p>Then there are the matchers. </p>
  
  <blockquote>
    <p>FlannBased, BruteForce, knnMatch and probably some others.</p>
  </blockquote>
  
  <p>After some reading, I figured that certain matchers can only be used with certain extractors as explained here. <a href="https://stackoverflow.com/questions/7232651/how-does-opencv-orb-feature-detector-work">How Does OpenCV ORB Feature Detector Work?</a>
  The classification given is quite clear but it's only for a few extractors and I don't understand the difference between float and uchar. </p>
  
  <p>So basically, can someone please </p>
  
  <ol>
  <li>classify the types of detectors, extractors and matchers based on float and uchar, as mentioned, or some other type of classification?</li>
  <li>explain the difference between the float and uchar classification or whichever classification is being used?</li>
  <li>mention how to initialize (code) various types of detectors, extractors and matchers?</li>
  </ol>
  
  <p>I know its asking for a lot but I'll be highly grateful.
  Thank you.</p>