deletions | additions       diff --git a/section_Methods_subsection_Algorithm_For__.tex b/section_Methods_subsection_Algorithm_For__.tex  index ccdb377..0a59011 100644  --- a/section_Methods_subsection_Algorithm_For__.tex  +++ b/section_Methods_subsection_Algorithm_For__.tex 
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\item assemble the vector $\vec{k}_{j}$ of values for a horizontal window of $n$ contiguous pixels $k_{[i,j-m \: : \: i,j+m]}$  \item repeat with $\vec{k}_{i}$, a vector of values for a vertical window of $n$ pixels $k_{[i-m,j \: : \: i+m,j]}$  \item both vectors are normalized, so they can be treated as distributions, and the Skewness $\gamma$ of each distribution (\textit{i.e.} a measure of the asymmetry in the gray-scale values in both direction) 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, $\mean(\gamma_j,  \gamma_i)$ \end{enumerate}  In order to compare results from a range of pictures the window size will not be reported in pixels, but as the number of windows along the longest side of the image. For example, if a picture is 640 $\times$ 480 pixels and the \textsc{skv} is calculated with a window size of 64 pixels then this will be written as \textsc{skv}$_{10}$ as there are 10 windows along the longest side.