title: Harris Laplacian Detector and auto scale selection
body: <p>I do want to get a basic understanding of the Harris Laplacian Detector. I do know the Harris detector but cannot figure out the idea behind the Harris Laplacian Detector.</p>
  
  <p>Let's assume we have two images showing pretty much the same thing but one image is scaled.
  We want to find all interest points so we can check them later for correspondance and compute a homography matrix later. Obviously we need a scale invariante interest point detector. </p>
  
  <p>What I think the Harris-Laplacian works (very very abstract; correct me if I'm wrong):</p>
  
  <ul>
  <li>Apply Harris detector to both images</li>
  <li>Apply a function to each interest points which were returned by the Harris detector</li>
  <li>Keep all points which maximize the Laplacian </li>
  <li>Scale images and apply Harris detector again to both images</li>
  <li>...repeat other steps...</li>
  </ul>
  
  <p>My questions:</p>
  
  <ol>
  <li>What kind of points maximizes the Laplacian? </li>
  <li>The function I apply to the interest points, what does it look like? Do I really apply it to the interest points or to a patch around the points?</li>
  <li>I've read the maxima of the function is used. What do we need it for, what does it tell us?</li>
  <li>It seems this method is kind of bruteforce (scale images, apply harris, check which points to keep, scale images again, apply harris, ...). Isn't this very inefficient?</li>
  <li>The points which we want to keep have to maximize the Laplacian. What is 'the Laplacian'? Is it the Laplacian operator?</li>
  <li>If a point maximizes the Laplacian we have a couple of points for each scale. How do we get the final interest points - from which scale do we get them?</li>
  <li>When does the algorithm terminate?</li>
  <li>Why do we use Laplacian?</li>
  </ol>
  
  <p>Pretty many questions :/</p>