deletions | additions       diff --git a/section_Methods_subsection_Algorithm_For__.tex b/section_Methods_subsection_Algorithm_For__.tex  index a90b90f..0b56d26 100644  --- a/section_Methods_subsection_Algorithm_For__.tex  +++ b/section_Methods_subsection_Algorithm_For__.tex 
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\item extract the horizontal vector $\vec{k}_{h}$ of values for a window of $n$ pixels around the current position $k_{[i,j-m \: : \: i,j+m]}$  \item repeat with $\vec{k}_{v}$, a vector of values for a vertical window $k_{[i-m,j \: : \: i+m,j]}$  \item both vectors are normalized, so they can be treated as distributions, and the skewness of each distribution (\textit{i.e.} a measure of the asymmetry in the gray-scale values in both direction) is calculated, $\gamma_h$ and $\gamma_v$  \item the \textsc{skv} value of the current  pixel $\gamma_{i,j}$ is the mean of the two values $\frac{\gamma_h + \gamma_v}{2}$ \end{enumerate}  There will be anomalies in the results arising from the simplicity of this method, the most obvious of which is that features that are extended along the horizontal or vertical axis, but not both (that is thin horizontal and vertical lines) will enjoy a privileged position (see Figure~\ref{fig:cardol}). This problem, if it is a problem, can be easily overcome by employing a more sophisticated variant of the method.