Merge branch 'master' of github.com:taddeus/licenseplates

Conflicts:
	docs/verslag.tex

docs/verslag.tex

@@ -125,12 +125,11 @@ straight lines and simple curves, LBP should be suited to identify these.
 The LBP algorithm that we implemented is a square variant of LBP, the same
 that is introduced by Ojala et al (1994). Wikipedia presents a different
 form where the pattern is circular, this form is convenient because with
-interpolation you can choose the size of the circle \textbf{and} to how many
-neighbours the circle has. That means how many times the center pixel
-has to be evaluated against a neighbour.
+interpolation you can choose the size of the circle \textbf{and} how many
+neighbours the circle has.
 
 In the literature there are lots of examples where LBP is used for surface
-recognition, facial recognition, human face emotion recoqnition ((Pietik\"ainen, Hadid, Zhao \& Ahonen (2011))) 
+recognition, facial recognition, human face emotion recoqnition (Pietik\"ainen, Hadid, Zhao \& Ahonen (2011))
 \begin{itemize}
 \item Determine the size of the square where the local patterns are being
 registered. For explanation purposes let the square be 3 x 3. \\
@@ -195,7 +194,7 @@ Important to note is that due to the normalization of characters before
 applying LBP. Therefore, no further normalization is needed on the histograms.
 
 Given the LBP of a character, a Support Vector Machine can be used to classify
-the character to a character in a learning set. The SVM uses
+the character to a character in a learning set.
 
 \subsection{Matching the database}
 
@@ -205,6 +204,8 @@ histograms of an image as a feature vector.  The SVM can be trained with a
 subsection of the given dataset called the ''Learning set''. Once trained, the
 entire classifier can be saved as a Pickle object\footnote{See
 \url{http://docs.python.org/library/pickle.html}} for later usage.
+In our case a support vector machine uses a radial gauss kernel. The SVM finds
+a seperating hyperplane with minimum margins.
 
 
 
@@ -323,7 +324,6 @@ cells are related to one histogram. All the histograms are concatenated and
 feeded to the SVM that will be discussed in the next section, Classification.
 
 \subsection{Classification}
-The SVM used in our project is a Gaussian radial based function. Where the kernel is 
 
 
 \section{Finding parameters}