Currently, we have a constant co-efficent matrix, that is being utilized here for gaussian filtering. This also involves testing different map resolutions over different coefficients and also visualize the results if possible.
The standard deviation, plays an important role in determining the size of the gaussian kernel (or the coefficient matrix). As a rule of thumb according to this lecture, the size of the kernel, should be twice the size of the sigma or the standard deviation that will be used.
More to follow..
Currently, we have a constant co-efficent matrix, that is being utilized here for gaussian filtering. This also involves testing different map resolutions over different coefficients and also visualize the results if possible.
The standard deviation, plays an important role in determining the size of the gaussian kernel (or the coefficient matrix). As a rule of thumb according to this lecture, the size of the kernel, should be twice the size of the sigma or the standard deviation that will be used.
More to follow..