Add initial version of the introduction

mini_proj/report/waldo.tex

@@ -20,7 +20,7 @@
 	\usepackage{natbib}
 
 	\begin{document}
-		\title{What is waldo}
+		\title{What is Waldo?}
 		\author{Kelvin Davis \and Jip J. Dekker\and Anthony Silvestere}
 		\maketitle
 
@@ -30,13 +30,55 @@
 
 		\section{Introduction}
 
-		\section{Background}
+		Almost every child around the world knows about ``Where's Waldo?'', also
+		known as ``Where's Wally?'' in some countries. This famous puzzle book has
+		spread its way across the world and is published in more than 25 different
+		languages. The idea behind the books is to find the character ``Waldo'',
+		shown in \Cref{fig:waldo}, in the different pictures in the book. This is,
+		however, not as easy as it sounds. Every picture in the book is full of tiny
+		details and Waldo is only one out of many. The puzzle is made even harder by
+		the fact that Waldo is not always fully depicted, sometimes it is just his
+		head or his torso popping out from behind something else. Lastly, the reason
+		that even adults will have trouble spotting Waldo is the fact that the
+		pictures are full of ``Red Herrings'': things that look like (or are colored
+		as) Waldo, but are not actually Waldo.
 
-		\section{Methods}
+		\begin{figure}[ht]
+			\includegraphics[scale=0.35]{waldo}
+			\centering
+			\caption{A headshot of the character ``Waldo'', or ``Wally''. Pictures of
+			Waldo copyrighted by Martin Handford used under the fair-use policy.}
+			\label{fig:waldo}
+		\end{figure}
 
-		\section{Results and Discussion}
+		The task of finding Waldo is something that relates to a lot of real life
+		image recognition tasks. Fields like mining, astronomy, surveillance,
+		radiology, and microbiology often have to analyse images (or scans) to find
+		the tiniest details, sometimes undetectable by the human eye. These tasks
+		are especially hard when the thing(s) you are looking for are similar to the
+		rest of the images. These tasks are thus generally performed using computers
+		to identify possible matches.
 
-		\section{Conclusion}
+		``Where's Waldo?'' offers us a great tool to study this kind of problem in a
+		setting that is humanly tangible. In this report we will try to identify
+		Waldo in the puzzle images using different classification methods. Every
+		image will be split into different segments and every segment will have to
+		be classified as either being ``Waldo'' or ``not Waldo''. We will compare
+		various different classification methods from more classical machine
+		learning, like naive Bayes classifiers, to the currently state of the art,
+		Neural Networks. In \Cref{sec:background} we will introduce the different
+		classification methods, \Cref{sec:methods} will explain the way in which
+		these methods are trained and how they will be evaluated, in
+		\Cref{sec:results} will discuss the results, and \Cref{sec:conclusion} will
+		offer our final conclusions.
+
+		\section{Background} \label{sec:background}
+
+		\section{Methods} \label{sec:methods}
+
+		\section{Results and Discussion} \label{sec:results}
+
+		\section{Conclusion} \label{sec:conclusion}
 
 		\bibliographystyle{humannat}
 		\bibliography{references}