Filled in some sections of report.

docs/verslag.tex

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