What Is a Weighted Local Histogram? 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 $d(p_0,p_1) &lt; d(p_0,p_2)$. 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?
A weighted local histogram would mean filtering the image $I(x,y)$ with a localised filter $H(x,y)$ (gaussian in this example). The resultant image is the 2D convolution $Y(x,y) = I(x,y)*H(x,y)$. The normal histogram of $Y(x,y)$ is the localised histogram of $I(x,y)$. It is used to view certain specific characteristics of an image depending on the filter used