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	\begin{document}
		\title{What is Waldo?}
		\author{Kelvin Davis \and Jip J. Dekker\and Anthony Silvestere}
		\maketitle

		\begin{abstract}

		\end{abstract}

		\section{Introduction}

		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.

		\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 and are used under the fair-use policy.
			}
			\label{fig:waldo}
		\end{figure}

		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.

		``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}

		The classification methods used can separated into two separate groups:
		classical machine learning methods and neural network architectures. Many of
		the classical machine learning algorithms have variations and improvements
		for various purposes; however, for this report we will be using their only
		their basic versions. In contrast, we will use different neural network
		architectures, as this method is currently the most used for image
		classification.

		\subsection{Classical Machine Learning Methods}

		\paragraph{Naive Bayes Classifier}

		\cite{naivebayes}

		\paragraph{$k$-Nearest Neighbors}

		($k$-NN) \cite{knn}

		\paragraph{Support Vector Machine}

		\cite{svm}

		\paragraph{Random Forest}

		\cite{randomforest}

		\subsection{Neural Network Architectures}
		\todo{Did we only do the three in the end? (Alexnet?)}

		\paragraph{Convolutional Neural Networks}

		\paragraph{LeNet}

		\paragraph{Fully Convolutional Neural Networks}


		\section{Methods} \label{sec:methods}

		\section{Results and Discussion} \label{sec:results}

		\section{Conclusion} \label{sec:conclusion}

		\bibliographystyle{alpha}
		\bibliography{references}

	\end{document}