In the last post, we saw how convolutions could be used to sharpen an image by looking for areas of contrast. Using a similar intuition, we now consider ways to detect edges in an image. Detecting edges can be an important building block for tasks processing tasks like feature detection, and extraction.
While many different methods, and models exist for detecting edges, one simple way is to look for areas of fast changes in pixel gradient, i.e., looking at derivatives (calculus) in an image. The idea is that an edge is more likely occur where there are large sudden changes in contrast.