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

docs/report.tex

@@ -641,23 +641,43 @@ to help out. Further communication usually went through e-mails and replies
 were instantaneous! A crew to remember.
 
 \section{Discussion}
+
+There are a few points open for improvement. These are the following.
+
 We had some good results but of course there are more things to explore.
-For instance we did a research on three different patterns. There are more patterns
-to try. For instane we only tried (8,3)-, (8,5)- and (12,5). The interesting to
-do is to test which pattern gives the best result. This is also done by grid-
-searching, changing the size of circle and the amount of neighbours.
+For instance we did a research on three different patterns. There are more
+patterns to try. For instance we only tried (8,3)-, (8,5)- and
+(12,5)-neighbourhoods. What might be done is to test which pattern gives the
+best result, for a wider range of neighbourhoods. We haven proven that the size
+and number of points do influence the performance of the classifier, so further
+research would be in place.
 
 One important feature of our framework is that the LBP class can be changed by
-an other technique. This may be a different algorithm than LBP. Also the classifier
-can be changed in an other classifier. By applying these kind of changes we can
-find the best way to recognize licence plates.
-
-We don't do assumption when a letter is recognized. For instance dutch licence plates
-exist of three blocks, two digits or two characters. Or for the new licence plates
-there are three blocks, two digits followed by three characters, followed by one or
-two digits. The assumption we can do is when there is have a case when one digit
-is moste likely to follow by a second digit and not a character. Maybe these assumption
-can help in future research to achieve a higher accuracy rate.
+an other technique. This may be a different algorithm than LBP. Also the
+classifier can be changed in an other classifier. By applying these kind of
+changes we can find the best way to recognize licence plates.
+
+We don't do assumption when a letter is recognized. For instance Dutch licence
+plates exist of three blocks, two digits or two characters. Or for the new
+licence plates there are three blocks, two digits followed by three characters,
+followed by one or two digits. The assumption we can do is when there is have a
+case when one digit is most likely to follow by a second digit and not a
+character. Maybe these assumption can help in future research to achieve a
+higher accuracy rate.
+
+A possibility to improve the performance speedwise would be to separate the
+creation of the Gaussian kernel and the convolution. This way, the kernel can
+be cached, which is a big improvement. At this moment, we calculate this kernel
+every time a blur is applied to a character. This was done so we could use a
+standard Python function, but we realised too late that there is performance
+loss due to this.
+
+Another performance loss was introduced by checking for each pixel if it is
+in the image. This induces a lot of function calls and four conditional checks
+per pixel. A faster method would be to first set a border of black pixels around
+the image, so the inImage function is now done implicitly because it simply
+finds a black pixel if it falls outside the original image borders.
+
 
 \appendix
 

docs/taddeus_presentatie.tex

@@ -0,0 +1,107 @@
+\documentclass{beamer}
+\mode<presentation>
+\setbeamertemplate{footline}[page number]
+
+\author{
+  Gijs van der Voort\\
+  Fabi\"en Tesselaar\\
+  Richard Torenvliet\\
+  Tadde\"us Kroes\\
+  Jayke Meijer
+}
+
+\title{Character Recognition with Local Binary Patterns}
+
+\begin{document}
+
+  \begin{frame}
+    \titlepage
+  \end{frame}
+
+  \section{From local pattern to feature vector}
+		
+  \begin{frame}
+    \frametitle{From local pattern to feature vector}
+
+    \begin{itemize}
+    \item Uitleggen lbp en hoe onze feature vector eruit ziet
+    \pause
+    \item Dus 8 punten en waar die vandaan komen
+    \end{itemize}
+  \end{frame}
+
+  \section{Setting up the SVM}
+
+  \begin{frame}
+    \frametitle{Lib SVM}
+
+    \begin{itemize}
+      \item Wat moet je aanroepen
+      \pause
+      \item Wat kun je instellen .. evt nog meer?
+    \end{itemize}
+  \end{frame}
+
+  \section{Finding the correct SVM parameters}
+
+  \begin{frame}
+    \frametitle{Finding the correct SVM parameters}
+    \begin{itemize}
+      \item Waarom zijn deze belangrijk
+      \pause
+      \item Wat is het doel van goede parameters
+      \pause
+      \item hoe verschilt een slechte van goede 
+      \pause
+      \item hoe vindt je de juiste
+    \end{itemize}
+  \end{frame}
+
+  \section{Training}
+
+  \begin{frame}
+    \frametitle{Training the SVM}
+    \begin{itemize}
+      \item Zorgen dat je een goede dataset hebt
+      \pause
+      \item Hoe je beste kunt trainen
+    \end{itemize}
+  \end{frame}
+
+  \section{Results}
+
+  \begin{frame}
+    \frametitle{Good results}
+    Goede resultaten goed.jpg
+  \end{frame}
+
+  \begin{frame}
+    \frametitle{Bad results}
+    Slechte resultaten jammer.jpg
+  \end{frame}
+
+
+  \section{What can be improved}
+
+  \begin{frame}
+    \frametitle{What can be improved}
+    \begin{itemize}
+      \item Dat er 97 procent met blabla kan gehaald worden of zoiets
+      \pause
+      \item Dat het in C geschreven kan worden
+      \pause
+      \item Dat lib-svm met zijn printjes maf is
+    \end{itemize}
+  \end{frame}
+
+  \section{Conclusion} 
+    \begin{frame}
+      Van het project als geheel ook een beetje
+    \end{frame}
+
+  \section{References}
+	  \begin{frame}
+      Van het project als geheel ook een beetje
+    \end{frame}
+
+\end{document}