I'm reading through a couple of academic papers, and this terms often comes up "local weighted histogram". An example of quote is the following:
First, cumulative histograms are built for every pixel from its neighborhood, using Gaussian-neighborhood weighting
I would be able to construct a local histogram, I'm not sure what this weighting is about.
Say the central pixel is at $p_0 = (y_0,x_0)$, and we have two pixels of same gray scale value $r$ at coordinates $p_1 = (y_1,x_1)$ and $p_2 = (y_1,x_1)$, but such that $d(p_0,p_1) < d(p_0,p_2)$. A normal histogram would count that value $r$ twice. With a Gaussian weighting for example how would we count such pixel value in the histogram construction?