1
$\begingroup$

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?

Improve this question
$\endgroup$
1
  • 1
    $\begingroup$ "most of the modern edge detection algorithms use Laplacian with second derivation" [citation needed] -- I doubt this is true. $\endgroup$
    – Cris Luengo
    Feb 18, 2020 at 17:25

1 Answer 1

Reset to default
2
$\begingroup$

"Need" or "Want"?

Q1: The second derivative avoids the problem of a gradually changing color being "greyish".

Q2: Yes.

Here is one:

After advice about detecting focus quality of objects in a photo detected using YoloV3

It is based on how planar the color. Very planar means no edge.

There are many, many other variations of these techniques possible.

Improve this answer
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.