17
edits
Changes
→Calculating The Kernel
An even better way would be to integrate the Gaussian function instead of just taking point samples. Refer to the two graphs on the right.<br/>
The graphs plot the continuous distribution function and the discrete kernel approximation. One thing to look out for are the tails of the distribution vs. kernel supportweightt:<br/>
For the current configuration, we have 13.36% of the curve’s area outside the discrete kernel. Note that the weights are renormalized such that the sum of all weights is one. Or in other words:<br/>
the probability mass outside the discrete kernel is redistributed evenly to all pixels within the kernel. The weights are calculated by numerical integration of the continuous gaussian distribution<br/>
