87 lines
3.8 KiB
TeX
87 lines
3.8 KiB
TeX
\documentclass[a4paper]{article}
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% To compile PDF run: latexmk -pdf {filename}.tex
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\usepackage{graphicx} % Used to insert images into the paper
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\usepackage{float}
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\usepackage[justification=centering]{caption} % Used for captions
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\captionsetup[figure]{font=small} % Makes captions small
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\newcommand\tab[1][0.5cm]{\hspace*{#1}} % Defines a new command to use 'tab' in text
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% Math package
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\usepackage{amsmath}
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% Enable that parameters of \cref{}, \ref{}, \cite{}, ... are linked so that a reader can click on the number an jump to the target in the document
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\usepackage{hyperref}
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%enable \cref{...} and \Cref{...} instead of \ref: Type of reference included in the link
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\usepackage[capitalise,nameinlink]{cleveref}
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% UTF-8 encoding
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc} %support umlauts in the input
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% Easier compilation
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\usepackage{bookmark}
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\usepackage{natbib}
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\begin{document}
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\title{What is Waldo?}
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\author{Kelvin Davis \and Jip J. Dekker\and Anthony Silvestere}
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\maketitle
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\begin{abstract}
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\end{abstract}
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\section{Introduction}
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Almost every child around the world knows about ``Where's Waldo?'', also
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known as ``Where's Wally?'' in some countries. This famous puzzle book has
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spread its way across the world and is published in more than 25 different
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languages. The idea behind the books is to find the character ``Waldo'',
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shown in \Cref{fig:waldo}, in the different pictures in the book. This is,
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however, not as easy as it sounds. Every picture in the book is full of tiny
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details and Waldo is only one out of many. The puzzle is made even harder by
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the fact that Waldo is not always fully depicted, sometimes it is just his
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head or his torso popping out from behind something else. Lastly, the reason
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that even adults will have trouble spotting Waldo is the fact that the
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pictures are full of ``Red Herrings'': things that look like (or are colored
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as) Waldo, but are not actually Waldo.
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\begin{figure}[ht]
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\includegraphics[scale=0.35]{waldo}
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\centering
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\caption{A headshot of the character ``Waldo'', or ``Wally''. Pictures of
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Waldo copyrighted by Martin Handford used under the fair-use policy.}
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\label{fig:waldo}
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\end{figure}
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The task of finding Waldo is something that relates to a lot of real life
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image recognition tasks. Fields like mining, astronomy, surveillance,
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radiology, and microbiology often have to analyse images (or scans) to find
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the tiniest details, sometimes undetectable by the human eye. These tasks
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are especially hard when the thing(s) you are looking for are similar to the
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rest of the images. These tasks are thus generally performed using computers
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to identify possible matches.
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``Where's Waldo?'' offers us a great tool to study this kind of problem in a
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setting that is humanly tangible. In this report we will try to identify
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Waldo in the puzzle images using different classification methods. Every
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image will be split into different segments and every segment will have to
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be classified as either being ``Waldo'' or ``not Waldo''. We will compare
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various different classification methods from more classical machine
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learning, like naive Bayes classifiers, to the currently state of the art,
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Neural Networks. In \Cref{sec:background} we will introduce the different
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classification methods, \Cref{sec:methods} will explain the way in which
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these methods are trained and how they will be evaluated, in
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\Cref{sec:results} will discuss the results, and \Cref{sec:conclusion} will
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offer our final conclusions.
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\section{Background} \label{sec:background}
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\section{Methods} \label{sec:methods}
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\section{Results and Discussion} \label{sec:results}
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\section{Conclusion} \label{sec:conclusion}
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\bibliographystyle{humannat}
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\bibliography{references}
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\end{document}
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