I have been reading this http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm and I am having trouble to perform the seperability.
So I kinda did it in paper. Lets say y Gaussian function is G(X,Y), then seperating them will become G(X)G(Y), and then I will need to calculate the 1D component for X and 1D component for Y. Then I can pass over my image twice using the two components each time.
I do have a couple of questions though (one of them is more general):
1 - When I calculate the 2D Gaussian why do I need to normalize it by dividing each pixel of the template with the sum of the template?
2 - Do I need to do the same for the 1D components aswell?
3 - In the link it says:
Figure 4 shows the 1-D x component kernel that would be used to produce the full kernel shown in Figure 3 (after scaling by 273, rounding and truncating one row of pixels around the boundary because they mostly have the value 0. This reduces the 7x7 matrix to the 5x5 shown above.). The y component is exactly the same but is oriented vertically.
Why scale the 1D component by 273? 273 is the sum of the 2D template not the 1D. And how does he truncate 1 row? The 1D X component is 1 row by itself...
Thanks I hope my questions make sense.