tese-exemplo.tex

% Arquivo LaTeX de exemplo de dissertação/tese a ser apresentados à CPG do IME-USP
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% Versão 5: Sex Mar  9 18:05:40 BRT 2012
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% Criação: Jesús P. Mena-Chalco
% Revisão: Fabio Kon e Paulo Feofiloff
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% Obs: Leia previamente o texto do arquivo README.txt

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\begin{center}
    \vspace*{2.3cm}
    \textbf{\Large{Human skin segmentation\\[-0.25cm]
    using correlation rules on\\
    dynamic color clustering}}\\
    
    \vspace*{1.2cm}
    \Large{Rodrigo Augusto Dias Faria}
    
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    \textsc{
    Thesis submitted\\[-0.25cm]
    to the\\[-0.25cm]
    Institute of Mathematics and Statistics\\[-0.25cm]
    of the\\[-0.25cm]
    University of São Paulo\\[-0.25cm]
    to\\[-0.25cm]
    obtain the title\\[-0.25cm]
    of\\[-0.25cm]
    Master in Science}
    
    \vskip 1.5cm
    Program: Computer Science\\
    Advisor: Prof. Dr. Roberto Hirata Jr

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    \normalsize{São Paulo, August 2018}
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%        \textbf{\Large{Human skin segmentation using correlation rules\\ on dynamic color clustering}}\\
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        \textbf{\Large{Human skin segmentation\\[-0.25cm]
        using correlation rules on\\
        dynamic color clustering}}\\
        \vspace*{2 cm}
    \end{center}

    \vskip 2cm

    \begin{flushright}
    This version of the thesis contains the corrections and changes suggested by the\\
    Examining Committee during the defense of the original work, performed\\
    on August 31st, 2018. A copy of the original version is available at the\\
    Institute of Mathematics and Statistics of the University of São Paulo.

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    \begin{quote}
    \noindent Examining Committee:
    
    \begin{itemize}
 		\item Prof. Dr. Roberto Hirata Jr (advisor) - IME-USP
 		\item Prof. Dr. Manuel Menezes de Oliveira Neto - INF-UFRGS
 		\item Prof. Dr. João Eduardo Kogler Junior - EP-USP
    \end{itemize}
      
    \end{quote}
\pagebreak

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\begin{dedication}
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To my beloved wife and daughter.
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\end{dedication}

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\chapter*{Acknowledgements}
I would like to express my sincere gratitude to my advisor, Prof. Dr. Roberto Hirata Jr, who has believed in my potential since the beginning of my research. His motivation, patience, and enthusiasm in teaching me were essential during the accomplishment of all this work. More than that, this work would not have been done without his flexibility in admitting me as a part-time student to continue to work in the industry. We all know how difficult it is to reconcile these activities, but his comprehension is something that will be marked forever in my life. Thank you so much!

No matter how tired, discouraged, hopeless she was, but my wife Tamires gave me unconditional support. She accompanied me at absolutely every moment. She is the most incredible woman I have ever met. And, to my happiness, she gave me the greatest gift of my life: my daughter Júlia. I love you unconditionally.

I would also like to thank all my family, for the example they have always given me, for their care and dedication. In particular, I thank my mother for showing me the importance of education in my entire life. I have no doubt that your choices have always been thought the best for me and my siblings.

I can not forget two great friends I made during these years, among others who were part of this journey, Ana Lucia and Luiz Medina, for having spent so much time together and for providing discussions of the highest level, of the most varied subjects. They were also able to teach me the beauty of the proof by induction. I will always remember those days!

I should also remember the Professors who were members of my Examining Committee, Prof. Dr. Roberto Marcondes Cesar Junior and Prof. Dr. Peter Sussner, for kindly giving up their limited time to grant me valuable suggestions and corrections that have brought me to this result. In particular, to Prof. Dr. Roberto Marcondes for having indicated to me the reading of the paper that served as the basis for all this project.

I also thank the University of São Paulo for giving me the opportunity and for providing structure and excellence in teaching for the entire community. I may say many thanks also to everyone in the university who support us including, but not limited to, Professors, secretaries, co-workers, and colleagues.

And, last but not least, I would like to be grateful to Inatel and Qualcomm, the companies I have worked for during the years of the realization of this work. They were extremely supportive of me in all the difficult moments I faced during the course of this project.

You were all key players in this achievement!


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% Resumo
\chapter*{Resumo}

\noindent FARIA, R. A. D. \textbf{Segmentação de pele humana usando regras de correlação baseadas em agrupamento dinâmico de cores}.
Dissertação (Mestrado) - Instituto de Matemática e Estatística,
Universidade de São Paulo, São Paulo, 2018.
\\

A pele humana é constituída de uma série de camadas distintas, cada uma das quais reflete uma porção de luz incidente, depois de absorver uma certa quantidade dela pelos pigmentos que se encontram na camada. Os principais pigmentos responsáveis pela origem da cor da pele são a melanina e a hemoglobina. A segmentação de pele desempenha um papel importante em uma ampla gama de aplicações em processamento de imagens e visão computacional. Em suma, existem três abordagens principais para segmentação de pele: baseadas em regras, aprendizado de máquina e híbridos. Elas diferem em termos de precisão e eficiência computacional. Geralmente, as abordagens com aprendizado de máquina e as híbridas superam os métodos baseados em regras, mas exigem um conjunto de dados de treinamento grande e representativo e, por vezes, também um tempo de classificação custoso, que pode ser um fator decisivo para aplicações em tempo real. Neste trabalho, propomos uma melhoria, em três versões distintas, de um novo método de segmentação de pele baseado em regras que funciona no espaço de cores YCbCr. Nossa motivação baseia-se nas hipóteses de que: (1) a regra original pode ser complementada e, (2) pixels de pele humana não aparecem isolados, ou seja, as operações de vizinhança são levadas em consideração. O método é uma combinação de algumas regras de correlação baseadas nessas hipóteses. Essas regras avaliam as combinações de valores de crominância Cb, Cr para identificar os pixels de pele, dependendo da forma e tamanho dos agrupamentos de cores de pele gerados dinamicamente. O método é muito eficiente em termos de esforço computacional, bem como robusto em imagens muito complexas.
\\

\noindent \textbf{Palavras-chave:} detecção de pele, segmentação de pele humana, modelo de cores YCbCr, regras de correlação, agrupamento dinâmico de cores.

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% Abstract
\chapter*{Abstract}
\noindent FARIA, R. A. D. \textbf{Human skin segmentation using correlation rules on dynamic color clustering}.
Thesis (Master's Degree) - Institute of Mathematics and Statistics,
University of São Paulo, São Paulo, 2018.
\\

Human skin is made of a stack of different layers, each of which reflects a portion of impinging light, after absorbing a certain amount of it by the pigments which lie in the layer. The main pigments responsible for skin color origins are melanin and hemoglobin. Skin segmentation plays an important role in a wide range of image processing and computer vision applications. In short, there are three major approaches for skin segmentation: rule-based, machine learning and hybrid. They differ in terms of accuracy and computational efficiency. Generally, machine learning and hybrid approaches outperform the rule-based methods but require a large and representative training dataset and, sometimes, costly classification time as well, which can be a deal breaker for real-time applications. In this work, we propose an improvement, in three distinct versions, of a novel method for rule-based skin segmentation that works in the YCbCr color space. Our motivation is based on the hypotheses that: (1) the original rule can be complemented and, (2) human skin pixels do not appear isolated, i.e. neighborhood operations are taken into consideration. The method is a combination of some correlation rules based on these hypotheses. Such rules evaluate the combinations of chrominance Cb, Cr values to identify the skin pixels depending on the shape and size of dynamically generated skin color clusters. The method is very efficient in terms of computational effort as well as robust in very complex images.
\\

\noindent \textbf{Keywords:} skin detection, human skin segmentation, YCbCr color model, correlation rules, dynamic color clustering.

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% Sumário
\tableofcontents    % imprime o sumário

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\chapter{List of Acronyms}
\begin{tabular}{ll}
    AR          & Aleix and Robert Face Database\\
    CIE         & Commission Internationale de l'Eclairage\\
    CMY         & Cyan, Magenta and Yellow\\
    FERET       & Face Recognition Technology database\\
    GIS         & Geographic Information System\\
    HGR         & Hand Gesture Recognition database\\
    HSI         & Hue, Saturation, Intensity\\
    HSL         & Hue, Saturation, Lightness\\
    HSV         & Hue, Saturation, Value\\
    IHLS        & Improved, Hue, Luminance and Saturation\\
    LUT         & Look-UP Table\\
    NTSC        & National Television System Committee\\
    PAL         & Phase Alternating Line\\
    RGB         & Red, Green and Blue\\
    SCT         & Scitex Continuous Tone Format\\
    SECAM       & Sequential Color with Memory\\
    SFA         & Skin of FERET and AR Database\\
    UCS         & Uniform Chromaticity Scale\\
    UV          & Ultra Violet\\
    VISAPP      & International Joint Conference on Computer Vision, Imaging and Computer\\
                & Graphics Theory and Applications\\
    YIQ         & Luma, Hue and Saturation\\
    YUV         & Luma and Chrominance\\
\end{tabular}

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\chapter{List of Symbols}
\begin{tabular}{ll}
    $f(x, y)$   & Intensity function of an image \\
    $H$         & Number of rows of an image \\
    $W$         & Number of columns of an image \\
    $L$         & Number of gray levels \\
    $L_{min}$   & The minimum gray level of a range \\
    $L_{max}$   & The maximum gray level of a range \\
    $L_n$       & The $n$-th channel of a pixel \\
    $L_i$       & The $i$-th channel of a pixel \\
    $L^i_{min}$ & The minimum gray level of the $i$-th channel of a pixel \\
    $L^i_{max}$ & The maximum gray level of the $i$-th channel of a pixel \\
    $N_4(p)$    & Four neighbors of a pixel $p$ \\
    $N_D(p)$    & Diagonal neighbors of a pixel $p$ \\
    $N_8(p)$    & Eight neighbors of a pixel $p$ \\
    $AND$       & AND logic operator \\
    $OR$        & OR logic operator \\
    $XOR$       & XOR logic operator \\
    $NOT$       & NOT logic operator \\
    $T$         & Threshold value in image histogram split \\
    $T_n$       & Multi-threshold values in image histogram split \\
    $L^*$       & Luminance \\
    $a^*$       & Green/red axis on $L^*a^*b^*$ color model \\
    $b^*$       & Blue/yellow axis on $L^*a^*b^*$ color model \\
    $u^*$       & Green/red axis on $L^*u^*v^*$ color model \\
    $v^*$       & Blue/yellow axis on $L^*u^*v^*$ color model \\
    $\theta$    & Hue angle on HSI color model \\
    $max$       & Max operator \\
    $min$       & Min operator \\
    $\argmax$   & Arguments of maxima operator \\
    $P$         & A point (pixel) in an image \\
    $P_Y$       & $Y$ component of a pixel $P$ in $YCbCr$ model \\
\end{tabular}
\clearpage
\begin{tabular}{ll}
    $Y_{Cb}$    & Subspace of $Cb$ points in function of $Y$ \\
    $Y_{Cr}$    & Subspace of $Cr$ points in function of $Y$ \\
    $T_{YCb}$   & Trapezoid of the $Y_{Cb}$ subspace \\
    $T_{YCr}$   & Trapezoid of the $Y_{Cr}$ subspace \\
    $A_{T_{YCb}}$& Area of $Y_{Cb}$ trapezoid \\
    $A_{T_{YCr}}$& Area of $Y_{Cr}$ trapezoid \\
    $Y_{min}$   & Minimum value of $Y$ luminance distribution \\
    $Y_{max}$   & Maximum value of $Y$ luminance distribution \\
    $Y_{0}$     & Top-left coordinate of $T_{YCr}$ trapezoid \\
    $Y_{1}$     & Top-right coordinate of $T_{YCr}$ trapezoid \\
    $Y_{2}$     & Top-left coordinate of $T_{YCb}$ trapezoid \\
    $Y_{3}$     & Top-right coordinate of $T_{YCb}$ trapezoid \\
    $Y_{min}$   & Minimum value of $Y$ component \\
    $Y_{max}$   & Maximum value of $Y$ component \\
    $Cr_{min}$  & Minimum $Cr$ value used to compute $T_{YCr}$ trapezoid \\
    $Cr_{max}$  & Maximum $Cr$ value used to compute $T_{YCr}$ trapezoid \\
    $Cb_{min}$  & Minimum $Cb$ value used to compute $T_{YCb}$ trapezoid \\
    $Cb_{max}$  & Maximum $Cb$ value used to compute $T_{YCb}$ trapezoid \\
    $h_{Cr}$    & Height of $T_{YCr}$ trapezoid \\
    $h_{Cb}$    & Height of $T_{YCb}$ trapezoid \\
    $H_{Cr}(P_Y)$& Height of other $T_{YCr}$ coordinates \\
    $H_{Cb}(P_Y)$& Height of other $T_{YCb}$ coordinates \\
    $\Delta_{Cr}(P_Y)$& Distance between $(P_Y, H_{Cr}(P_Y))$ coordinate and the base of $T_{YCr}$ \\
    $\Delta_{Cb}(P_Y)$& Distance between $(P_Y, H_{Cb}(P_Y))$ coordinate and the base of $T_{YCb}$ \\
    $\Delta'_{Cr}(P_Y)$& Normalized distance with respect to the difference in size of the \\
                       &$T_{YCr}$ trapezoid \\
    $\Delta'_{Cb}(P_Y)$& Normalized distance with respect to the difference in size of the \\
                       &$T_{YCb}$ trapezoid \\
    $\alpha$    & Rate between $\Delta'_{Cr}(P_Y)$ and $\Delta'_{Cb}(P_Y)$ normalized distances \\
    $sf$        & Rate between the longer and shorter upper side of $T_{YCr}$ and $T_{YCb}$ \\
    $P_{Cr}$    & $Cr$ component of a pixel $P$ \\
    $P_{Cb}$    & $Cb$ component of a pixel $P$ \\
    $P_{Cr_{s}}$& Estimated value of $P_{Cr}$ point \\
    $P_{Cb_{s}}$& Estimated value of $P_{Cb}$ point \\
    $dP_{Cr_{s}}$& Estimated distance value of $P_{Cr}$ point \\
    $dP_{Cb_{s}}$& Estimated distance value of $P_{Cb}$ point \\
    $dP_{Cr}$   & Distance between $P_{Cr}$ point and $Cr_{min}$ \\
    $dP_{Cb}$   & Distance between $P_{Cb}$ point and $Cb_{max}$ \\
    $I_P$       & Minimum difference between $P_{Cr}$ and $P_{Cb}$ with respect to $P_{Cb_s}$ \\
\end{tabular}
\clearpage
\begin{tabular}{ll}
    $J_P$       & Maximum distance between the points $(P_Y, P_{Cb})$ and $(P_Y, P_{Cb_s})$ \\
    $I'_P$      & Minimum difference between $P_{Cr}$ and $P_{Cb}$ with respect to $P_{Cr_s}$ \\
    $J'_P$      & Maximum distance between the points $(P_Y, P_{Cr})$ and $(P_Y, P_{Cr_s})$ \\
    $P_{min}$   & Minimum percentile of the histogram of Y luminance component used to \\
                & compute trapezoid coordinates \\
    $P_{max}$   & Maximum percentile of the histogram of Y luminance component used to \\
                & compute trapezoid coordinates \\
\end{tabular}
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%\index{TBP|see{periodicidade região codificante}}
%\index{DSP|see{processamento digital de sinais}}
%\index{STFT|see{transformada de Fourier de tempo reduzido}}
%\index{DFT|see{transformada discreta de Fourier}}
%\index{Fourier!transformada|see{transformada de Fourier}}

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