X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/these_gilles.git/blobdiff_plain/de66b573538c6949bbc090e4e7ac4af93a868bbd..7a1f7981654e75ea330b58c7f93620ce69bf13b2:/THESE/these.tex

diff --git a/THESE/these.tex b/THESE/these.tex
index 21ae898..600f52a 100644
--- a/THESE/these.tex
+++ b/THESE/these.tex
@@ -1,12 +1,49 @@
 \documentclass[french]{spimufcphdthesis}
 \usepackage[utf8]{inputenc}
-            
+\usepackage[T1]{fontenc}
+             
 \usepackage{graphicx}
 \usepackage{color, xcolor}
 \usepackage[ruled,lined,linesnumbered]{algorithm2e}
+\usepackage{multirow}
+\usepackage{placeins}
   
 \usepackage{amsmath}
-\usepackage{booktabs}   
+\usepackage{booktabs}  
+\usepackage{textcomp}
+\usepackage{listings}
+
+\let\OLDlstinputlisting\lstinputlisting
+\renewcommand{\lstinputlisting}[2][]{\FloatBarrier\OLDlstinputlisting[#1]{#2}\FloatBarrier} 
+
+
+\lstset{morekeywords={HALF4,HALF3,float2,float3,float4,half,half2,half3,half4,tex2D,dim3,endif,threadIdx,blockIdx,blockDim,gridDim,Dim3,__host__,__global__,__shared__,float}}
+\lstset{
+  language=C,
+  columns=fixed,
+  basicstyle=\footnotesize\ttfamily,
+  numbers=left,
+  firstnumber=1,
+  numberstyle=\tiny,
+  stepnumber=5,             
+  numbersep=5pt,              
+  tabsize=3,                  
+  extendedchars=true,         
+  breaklines=true,       
+  keywordstyle=\textbf,
+  frame=single,         
+  % keywordstyle=[1]\textbf,   
+  %identifierstyle=\textbf,
+  commentstyle=\color{white}\textbf,
+  stringstyle=\color{white}\ttfamily,
+  % xleftmargin=17pt,
+  % framexleftmargin=17pt,
+  % framexrightmargin=5pt,
+  % framexbottommargin=4pt,
+  backgroundcolor=\color{lightgray},
+  basicstyle=\scriptsize,
+  texcl=true
+  }
 
 %%--------------------
 %% Set the title, subtitle, defense date, and
@@ -16,9 +53,9 @@
 %% The second mandatory parameter is the date of the PhD defense.
 %% The third mandatory parameter is the reference number given by
 %% the University Library after the PhD defense.
-\declarethesis[Sous-titre]{Titre}{17 septembre 2012}{XXX}
+\declarethesis[]{Algorithmes rapides pour le traitement des images bruitÃ©es sur GPU}{17 novembre 2013}{XXX}
       
-%%--------------------
+%%--------------------  
 %% Set the author of the PhD thesis
 \addauthor[gilles.perrot@univ-fcomte.fr]{Gilles}{Perrot}
     
@@ -31,25 +68,32 @@
    
 %%--------------------
 %% Set the laboratory where the thesis was made
-%\addlaboratory{Laboratoire Waynes Industry}
+\addlaboratory{Institut FEMTO-ST, dÃ©partement DISC}
   
 %%-------------------- 
 %% Set the English abstract
-\thesisabstract[english]{This is the abstract in English}
-       
+\thesisabstract[english]{
+%Recent graphical processing units (GPU), bring parallel computing capabilities to almost every developper, 
+In theory, modern graphical processing units (GPUs) make parallel programming accessible to all, and have triggered widespread interest  among researchers or developers  of all disciplines, with the hope of dramatically increasing processing speeds. Nevertheless, obtaining such performances cannot be done without considerable designing efforts : as an answer, we propose two GPU-based methods leading to fast implementations of several algorithms targeted to processing noisy images. One of them consists in porting the segmentation algorithm named \textit{snake}, with the effect of extending  its processing capacity and performance. A second involves a innovative GPU-specific algorithm, based on searching for level lines within gray-level or color images to reduce gaussian noise, whose quality-to-speed ratio is particularly interesting.
+Through extremely fine-tuned management of the different memory types available on GPUs, we have also conferred unprecedent flow rates to the median filter, making it able to process over 5 billion pixels per second. Eventually, we extended the above methods to the more generic convolution filter, and showed they out-perform the fastest implementations known to date, with over 7 billion pixels per second. In addition, we provide an on-line application that enables any developer to automatically generate operational source code of our filters.
+} 
 %%--------------------   
 %% Set the English keywords. They only appear if
 %% there is an English abstract
-\thesiskeywords[english]{Keyword 1, Keyword 2}
+\thesiskeywords[english]{GPU, Filtering, Image, Segmentation}
     
 %% --------------------
 %% Set the French abstract
-\thesisabstract[french]{Ceci est le rÃ©sumÃ© en franÃ§ais}
+\thesisabstract[french]{
+ Les cartes graphiques modernes (GPU) mettent, en thÃ©orie, la programmation parallÃ¨le Ã  la portÃ©e de tous. Ces facilitÃ©s ont Ã©veillÃ© l'intÃ©rÃªt des chercheurs et dÃ©veloppeurs de toutes disciplines, qui ont tentÃ© de tirer parti des performances Ã©levÃ©es de ces matÃ©riels. Cependant, d'importants efforts de conception sont souvent nÃ©cessaires Ã  l'obtention des vitesses de traitement espÃ©rÃ©es.
+Dans cette thÃ¨se, nous proposons des mÃ©thodes conduisant Ã  des implÃ©mentations rapides de plusieurs algorithmes destinÃ©s au traitement des images fortement bruitÃ©es. La premiÃ¨re est une transposition sur GPU d'un algorithme de segmentation dit du \textit{snake} dont la capacitÃ© de traitement a Ã©tÃ© Ã©tendue et les performances amÃ©liorÃ©es. La seconde dÃ©crit un algorithme original, basÃ© sur la recherche des lignes de niveaux et conÃ§u spÃ©cifiquement pour les GPUs, qui rÃ©duit le bruit gaussien dans les images en niveaux de gris ou en couleur et dont le rapport qualitÃ©/vitesse est particuliÃ¨rement intÃ©ressant. En concevant une gestion fine des mÃ©moires du GPU, nous avons Ã©galement confÃ©rÃ© un dÃ©bit de traitement inÃ©galÃ© au filtre mÃ©dian, pouvant dÃ©passer les 5 milliards de pixels Ã  la seconde. Enfin nous avons Ã©tendu l'application de ces techniques Ã  un opÃ©rateur beaucoup plus gÃ©nÃ©rique, le filtre de convolution, et montrÃ© qu'elles permettaient de surpasser les implÃ©mentations les plus rapides connues jusqu'alors, avec un maximum au delÃ  des 7 milliards de pixels Ã  la seconde. Nous mettons aussi Ã  disposition une application en ligne permettant Ã  tout dÃ©veloppeur de gÃ©nÃ©rer les codes sources opÃ©rationnels des filtres que nous avons dÃ©crits.               
+}
+
  
 %%--------------------
 %% Set the French keywords. They only appear if
 %% there is an French abstract
-\thesiskeywords[french]{Mot-cl\'e 1, Mot-cl\'e 2}
+\thesiskeywords[french]{GPU, Filtrage, Image, Segmentation}
  
 %%--------------------
 %% Change the speciality of the PhD thesis
@@ -76,6 +120,13 @@
 
 
 %\declareupmtheorem{rq}{Remarque}{Liste des remarques}
+
+
+\newcommand{\kl}{\includegraphics[scale=0.7]{kernLeft.png}~}
+\newcommand{\kr}{\includegraphics[scale=0.7]{kernRight.png}}
+\usepackage{bbold}
+\def\indentit{\mbox{l\hspace{-0.55em}1}}
+
    
 \begin{document}
     
@@ -87,40 +138,41 @@
 % annonce du plan  
 \chapter{Introduction}          
 \input{Chapters/chapter1/chapter1.tex}
-\chapter{Le traitement des images bruitÃ©es}   
+\chapter{Les processeurs graphiques (GPU) NVidia\textregistered}\label{ch-GPU}
+\input{Chapters/chapter1b/chapter1b.tex}
+\part*{Le traitement des images}
+\section*{Introduction} 
 \input{Chapters/chapter2/chapter2.tex}
-% gÃ©nÃ©raliÃ©ts
-%classes d'algo, outils ( histo, PDF, gauss, speckle, etc..)
-%\section{L'Ã©tat de l'art des implÃ©mentations GPU}
-% segmentation
-% filtrage : rÃ©duction de bruit, convolutions 
-% tracking         
-% pattern recognition 
-\chapter{La segmentation par snake polygonal orientÃ© rÃ©gions}
+\chapter{ModÃ¨les d'image et de bruits - notations}
+\input{Chapters/chapter2/chapter2b.tex}
+\chapter{Les techniques de rÃ©duction de bruit\label{ch-filtrage}}
+\input{Chapters/chapter2/chapter2c.tex}
+\chapter{Les techniques de segmentation des images}
+\input{Chapters/chapter2/chapter2d.tex}
+
+\part*{Algorithmes GPU rapides pour la rÃ©duction de bruit et la segmentation}
+\chapter{La segmentation par snake polygonal orientÃ© rÃ©gions\label{ch-snake}}
 \input{Chapters/chapter3/chapter3.tex}     
-% segmentation, intÃ©rÃªt, domaines
-% notations, formules   
-% snake polygonal 
-%                
-% dÃ©tails        
-% rÃ©sultats, images
-% --> multitarget
- 
-\chapter{Le filtrage des images sur GPU}
-\section{Algorithme de rÃ©duction de bruit par recherche des lignes de niveaux}
-% dÃ©tails : bruits AWGN, MGN, speckle, images couleur
-% rÃ©sultats, conclusion 
-\section{Filtre mÃ©dian}  
-% bruit SAP, origine, importance       
-% dÃ©tails, rÃ©sultats
-\section{Filtres de convolution}   
+
+\chapter{RÃ©duction de bruit par recherche des lignes de niveaux\label{ch-lniv}}
+\input{Chapters/chapter4/chapter4.tex}
+
+\chapter{Le filtre mÃ©dian sur GPU\label{ch-median}} 
+\input{Chapters/chapter5/chapter5.tex}
+
+\chapter{Les filtres de convolution sur GPU\label{ch-convo}}
+\input{Chapters/chapter6/chapter6.tex} 
 % prÃ©sentation, rÃ´le   
 % dÃ©tails : convo NON SEP, convo SEP, discussion Shared mem
 % rÃ©sultats, conclusion  
 % intÃ©grer logiciels dans sections
 \chapter{Conclusion gÃ©nÃ©rale}
-% et perspectives 
-   
+\input{Chapters/chapter1/conclusion.tex}
+
+%\pagebreak
+%\section*{Remerciements}
+%\input{Chapters/chapter1/rem.tex}
+
 %--------------------
 % Bibliography, indexes...
 \bibliographystyle{plain}
