I am studying some trivial computer vision processing techniques and I came across edge detection algorithms. IMO sharp changes in the gradient are enough indications to detect the edges in an image but most of the modern edge detection algorithms use
Laplacian with second derivation to do the same task.
Q1. Are we using second derivative because the change in flux is a much better indicator for edges?
Q2. Can we use something other than Laplacian?