title: Edge Response Removal in SIFT
body: <p>In Lowe's paper Section 4.1 the ratio of principal curvatures using the Hessian Matrix is used to eliminate points that may belong to an edge. </p>
  
  <p>The paper does not specify whether the Hessian matrix is computed on the original image or the DoG. In <a href="http://blogs.oregonstate.edu/hess/code/sift/" rel="nofollow">Rob Hess's implementation</a> it is applied on the DoG at the specified octave &amp; interval. </p>
  
  <p>My question is <strong>why is the Hessian Matrix applied on the DoG</strong> to identify points that are potential edges? </p>
  
  <p>I understand from <a href="http://www.aishack.in/2010/05/sift-step-4-eliminate-edges-and-low-contrast-regions/" rel="nofollow">Utkarsh's tutorial</a> that the Hessian is supposed to be similar to Harris corner detection. The difference is that the Hessian matrix is a second-order derivative. So I concluded that the Hessian should be applied to the original image. </p>
  
  <p>Can you point me to relevant resources that explain how the Hessian is used? </p>