We have a image of a night sky (a bunch of bright stars) and I'm interested in applying an "arrow" filter for artistic effect. Notice that when I perform a convolution, the arrows are pointing in the same direction as my kernel. When performing a correlation, the arrows are actually pointing in the opposite direction. So the sign used in this case is important, because the kernel is not symmetric.
So what is the difference between using a convolution vs. correlation to do image filtering? Well, here's one example.
Click here for full resolution image. Note that image is 200 x 200, kernel is 9 x 9.
We have a image of a night sky (a bunch of bright stars) and I'm interested in applying an "arrow" filter for artistic effect. Notice that when I perform a convolution, the arrows are pointing in the same direction as my kernel. When performing a correlation, the arrows are actually pointing in the opposite direction. So the sign used in this case is important, because the kernel is not symmetric.