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@@ -135,15 +135,53 @@ choices we made.
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\subsection*{Licenseplate retrieval}
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+In order to retrieve the license plate from the entire image, we need to perform
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+a perspective transformation. However, to do this, we need to know the
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+coordinates of the four corners of the licenseplate. For our dataset, this is
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+stored in XML files. So, the first step is to read these XML files.
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+\paragraph*{XML reader}
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+
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+
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+
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+\paragraph*{Perspective transformation}
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+
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+Once we retrieved the cornerpoints of the licenseplate, we feed those to a
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+module that extracts the (warped) licenseplate from the original image, and
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+creates a new image where the licenseplate is cut out, and is transformed to a
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+rectangle.
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\subsection*{Noise reduction}
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+The image contains a lot of noise, both from camera errors due to dark noise etc.,
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+as from dirt on the license plate. In this case, noise therefor means any unwanted
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+difference in color from the surrounding pixels.
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+\paragraph*{Camera noise and small amounts of dirt}
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-\subsection*{Character retrieval}
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+The dirt on the licenseplate can be of different sizes. We can reduce the smaller
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+amounts of dirt in the same way as we reduce normal noise, by applying a gaussian
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+blur to the image. This is the next step in our program.\\
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+\\
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+The gaussian filter we use comes from the \texttt{scipy.ndimage} module. We use
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+this function instead of our own function, because the standard functions are
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+most likely more optimized then our own implementation, and speed is an important
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+factor in this application.
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+\paragraph*{Larger amounts of dirt}
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+Larger amounts of dirt are not going to be resolved by using a Gaussian filter.
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+We rely on one of the characteristics of the Local Binary Pattern, only looking at
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+the difference between two pixels, to take care of these problems.\\
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+Because there will probably always be a difference between the characters and the
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+dirt, and the fact that the characters are very black, the shape of the characters
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+will still be conserved in the LBP, even if there is dirt surrounding the character.
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+
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+\subsection*{Character retrieval}
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+
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+The retrieval of the character is done the same as the retrieval of the license
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+plate, by using a perspective transformation. The location of the characters on the
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+licenseplate is also available in de XML file, so this is parsed from that as well.
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\subsection*{Creating Local Binary Patterns and feature vector}
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@@ -155,7 +193,18 @@ choices we made.
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\section{Finding parameters}
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-
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+Now that we have a functioning system, we need to tune it to work properly for
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+license plates. This means we need to find the parameters. Throughout the program
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+we have a number of parameters for which no standard choice is available. These
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+parameters are:\\
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+\\
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+\begin{tabular}{l|l}
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+ Parameter & Description\\
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+ \hline
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+ $\sigma$ & The size of the gaussian blur.\\
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+ \emph{cell size} & The size of a cell for which a histogram of LBPs will be generated.
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+
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+\end{tabular}
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\section{Conclusion}
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Jayke Meijer