Isn't there some sort of pre-processing of images where you can get all the gradients/edges for use in the classification or regression of images?
Like histograms of oriented gradients - is this something similar?
herojelly
You can tell in these image that Gy is really good at capturing the horizontal lines (since these areas have large vertical changes) and Gx is really good at capturing vertical lines (since these areas have large horizontal changes).
ioannis-vm
The operation G=√Gx2+Gy2 is called the Geometric Mean, with numerous applications ranging from social science to earthquake engineering. In the latter, we use it to combine ground motion acceleration time-histories recorded in two orthogonal directions to obtain a univariate time-history representative of that earthquake.
j316chuck
What happens if edges are purely diagonal? Will the Gx and Gy still capture the right responses?
Isn't there some sort of pre-processing of images where you can get all the gradients/edges for use in the classification or regression of images?
Like histograms of oriented gradients - is this something similar?
You can tell in these image that Gy is really good at capturing the horizontal lines (since these areas have large vertical changes) and Gx is really good at capturing vertical lines (since these areas have large horizontal changes).
The operation G=√Gx2+Gy2 is called the Geometric Mean, with numerous applications ranging from social science to earthquake engineering. In the latter, we use it to combine ground motion acceleration time-histories recorded in two orthogonal directions to obtain a univariate time-history representative of that earthquake.
What happens if edges are purely diagonal? Will the Gx and Gy still capture the right responses?