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\section{Methods}  The simplest form of the \textsc{skv} 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 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 $\vec{k}_{j}$ of values for a window of $n$ pixels $k_{[i,j; i,j+1; i,j+n-1]}$ along in  the horizontal direction  \item repeat with $\vec{k}_{i}$ of values for a window of $n$ pixels $k_{[i,j; i+1,j; i+n-1,j]}$ along vertically  \item both vectors are normalized, so they can be treated as distributions, and  the vertical Skewness $\gamma$ of each distribution is calculated, $\gamma_j$ and $\gamma_i$  \end{enumerate}