deletions | additions       diff --git a/section_Methods_The_abbreviation_textsc__.tex b/section_Methods_The_abbreviation_textsc__.tex  index f503503..fc3831d 100644  --- a/section_Methods_The_abbreviation_textsc__.tex  +++ b/section_Methods_The_abbreviation_textsc__.tex 
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The simplest form of the algorithm was used for all the results in this paper. For results derived from monochrome images each pixel is defined by three values $i,j,k$ where $i$ and $j$ are the row and column positions of the pixel respectively, and $k$ is the value of the pixel on the greyscale from 0 (black) to 1 (white). Images are processed on a pixel-by-pixel basis and the \textsc{skv} value of each pixels is calculated thus:  \begin{enumerate}  \item assemble the vector $\vec{k}_{j}$ of values for a horizontal window of $n$ contiguous pixels $k_{[i,j; i,j+1; i,j+n-1]}$  \item repeat with $\vec{k}_{i}$ $\vec{k}_{i}$, a vector  of values for a vertical window of $n$ pixels $k_{[i,j; i+1,j; i+n-1,j]}$ \item both vectors are normalized, so they can be treated as distributions, and the Skewness $\gamma$ of each distribution is calculated, $\gamma_j$ and $\gamma_i$  \item the \textsc{skv} value of the pixel is the larger of the two values $\max(\gamma_j, \gamma_i)$  \end{enumerate}