final rapporteurs ?

diff --git a/THESE/Chapters/chapter1/rem.tex b/THESE/Chapters/chapter1/rem.tex
index 98403a875d3addf1a6518425f36c5106f0945da2..f532f69b92e6deea6a7a9a5205dbad84d608b10c 100644 (file)
--- a/THESE/Chapters/chapter1/rem.tex
+++ b/THESE/Chapters/chapter1/rem.tex
@@ -1,13 +1,14 @@
-La lecture du paragraphe consacré aux remerciements qui figure systématiquement dans les manuscrits qu'il m'a été donné de lire m'a toujours troublé. Il revêt un caractère si personnel, voire émotionnel, qu'il ne me semblait pas avoir sa place dans un document à caractère scientifique et technique. Malgré cela, je me sens obligé, à mon tour, de faire subir ce désagrément au lecteur, tant le soutient dont j'ai bénéficié pendant ces années a été important. 
+La lecture du paragraphe consacré aux remerciements qui figure systématiquement dans les manuscrits qu'il m'a été donné de lire m'a toujours troublé. Il revêt un caractère si personnel, voire émotionnel, qu'il ne me semblait pas avoir sa place dans un document à caractère scientifique et technique. Malgré cela, je me sens obligé, à mon tour, de faire subir ce désagrément au lecteur, tant le soutient dont j'ai bénéficié pendant ces années a été déterminant. 
 
-Entreprendre une telle aventure après quinze années de carrière, en parallèle de mon activité d'enseignant, dans des conditions d'exercice souvent compliquées et éprouvantes, à fatalement imposé à mes proches, des désagréments voire des sacrifices. Il me tarde aujourd'hui de pouvoir leur rendre un peu de ce qu'il m'ont apporté pendant ces quatre années. 
+Entreprendre une telle aventure après quinze années de carrière, en parallèle de mon activité d'enseignant, dans des conditions d'exercice souvent compliquées et éprouvantes, à fatalement imposé à mes proches des désagréments, voire des sacrifices. Il me tarde aujourd'hui de pouvoir leur rendre un peu de ce qu'il m'ont apporté pendant ces quatre années. 
 
-La première ligne de cette équipe est naturellement constituée de mon épouse, incarnation de la compréhension,  et de ma fille, arrivée en cours de route, mais qui a immédiatement su préserver le sommeil réparateur de son vieil étudiant de père.
+La première ligne de cette équipe est naturellement constituée de mon épouse, incarnation de la compréhension, et de ma fille, arrivée en cours de route, mais qui a immédiatement su préserver le sommeil réparateur de son vieil étudiant de père et lui apporter un bonheur tel qu'il en est parvenu à faire s'évanouir toute lassitude.
 
 La deuxième ligne, plus discrète mais tout aussi vaillante, comprend tout d'abord le petit Dany, qui a consacré des dizaines d'heures à diverses relectures de prose anglaise ou française et attendu tout ce temps les travaux de bricolage que je lui ai promis. On y trouve aussi les amis Marc, Stéphanie, Nicolas, Muriel et leurs enfants qui nous ont permis de ne pas nous isoler, et aussi ma grand-mère, seul ancrage familial, qui nous a malheureusement quitté avant la fin.
 
-Dans les lignes arrières, on trouve les membres de l'équipe AND, pour leur l'accueil et la simplicité avec laquelle ils m'ont intégré dans leur fonctionnement, mais aussi, avec une mention spéciale pour Stéphane et Arnaud, pour toute l'assistance qu'il m'ont offerte ces dernières semaines dans mes nouveaux enseignements.
-Enfin, le capitaine, Raphaël qui ne savait vraisemblablement pas ce qu'il faisait lorsqu'il m'a ouvert la porte, mais sans qui je n'entreverrais certainement pas aujourd'hui le bout du tunnel.
+Dans les lignes arrières, les plus fournies, on trouve les membres de l'équipe AND, pour leur l'accueil et la simplicité avec laquelle ils m'ont intégré dans leur fonctionnement, avec des mentions spéciales pour Stéphane l'exigeant et Arnaud l'infatigable et disponible dépanneur, pour toute l'assistance qu'il m'ont offerte ces dernières semaines dans mes nouveaux enseignements. Je n'oublie pas les entraîneurs Michel et Alain qui m'ont concocté un emploi du temps sur mesure, et les remplaçants Jeff, David, Fabrice, Mourad, Jean-Luc, Karine et Abdallah qui, par petites touches de présence, d'échanges ou de partage ont tout aussi sûrement permis qu'à aucun moment je ne doute de mon choix. Merci enfin à Christian pour sa disponibilité, ses supports de cours et l'ensemble de ses conseils, il a rendu simple une année qui s'annonçait particulièrement difficile.  
+
+Enfin, le capitaine, Raphaël qui ne savait vraisemblablement pas ce qu'il faisait lorsqu'il m'a ouvert la porte, mais sans qui je n'entreverrais pas aujourd'hui le bout du tunnel.
 
          
 

diff --git a/THESE/Chapters/chapter1b/chapter1b.tex b/THESE/Chapters/chapter1b/chapter1b.tex
index c089c242510bce67040e305d7bd486d6128b2035..30662425430d0d802de075e84e966d63ca14499a 100644 (file)
--- a/THESE/Chapters/chapter1b/chapter1b.tex
+++ b/THESE/Chapters/chapter1b/chapter1b.tex
@@ -102,7 +102,7 @@ Les limitations de l'\textit{occupancy} ont pour origine :
 
 Nous verrons que cette notion d'\textit{occupancy}, si elle conserve du sens, peut toutefois être remise en question en optimisant d'autres aspects permettant d'arriver à une réduction de l'effet des latences, comme le parallélisme d'instructions ou l'augmentation du volume des transactions. En effet, ces techniques, et surtout l'utilisation avisée des différents types de mémoire du GPU permettent d'obtenir des performances élevées, parfois inenvisageables en suivant les prescriptions du constructeur.
 
-\section{Synthèse des contraintes d'implémentation}
+\section{Contraintes de conception}
 Certaines de ces contraintes ont déjà été évoquées rapidement, mais il nous semble important d'en donner ici une présentation synthétique. 
 \begin{enumerate}
 \item \textbf{Contiguïté}. 
@@ -119,5 +119,6 @@ Il est toutefois possible, lorsque la séquence de traitement le permet, de réa
 \item \textbf{Partage des ressources d'un SM}
 Le paragraphe sur l'\textit{occupancy} a abordé cet aspect par un exemple. Il faut retenir que chaque SM possède des ressources mémoire (registres, mémoire partagée) que les threads des blocs logiques de la grille de calcul se partagent au cours de l'exécution d'un kernel. L'équilibre entre l'utilisation de ces ressources et le dimensionnement de la grille de calcul relève d'un compromis parfois délicat à trouver pour obtenir les meilleures performances possibles. 
 \item \textbf{Les latences}. L'exécution des kernels subit l'effet de latences d'origines diverses. Les latences d'accès aux mémoires (voir Table \ref{tab-gpu-memoire}), les latences des différentes instructions arithmétiques ou encore les latences crées par l'inter dépendance d'instructions consécutives. Il est impératif de les prendre en considération et de mettre en \oe uvre des techniques adaptées pour les masquer au mieux.   
+\item \textbf{La mise au point}. L'ordonnancement des threads n'est pas prévisible et les quelques outils d'aide à la mise au point (debug) permettent simplement de cibler un  thread  présélectionné de la cible. Cela ne permet en aucun cas de déceler, par exemple, les conflits de banques provoqués par l'interaction d'au moins deux threads. Un outil de profilage développé par le fabricant fournit des informations importantes sur le nombre de conflits de banques et les origines probables des limitations de performance des kernels d'un programme. Il ne s'appuie cependant que sur un bloc de threads pour en extrapoler les résultats à l'ensemble de la grille.        
 \end{enumerate}   
-L'ensemble de ces aspects rend difficile la conception d'implémentations GPU rapides car rares sont les transcriptions directes d'un code CPU qui ne se heurtent pas sévèrement à l'une ou l'autre des contraintes que l'on vient d'énumérer. Les performances qui en résultent sont alors très en deça de celles attendues, voire inférieures à celles de l'implémentation CPU. 
+L'ensemble de ces aspects rend difficile la conception d'implémentations GPU rapides car rares sont les transcriptions directes d'un code CPU qui ne se heurtent pas sévèrement à l'une ou l'autre des contraintes que l'on vient d'énumérer. Les performances qui en résultent sont alors très en deça de celles attendues, voire inférieures à celles de l'implémentation CPU. La mise au point étant par ailleurs très délicate, il nous semble important de proposer des kernels élémentaires dont on peut aisément garantir les résultats par des méthodes de test ne nécessitant pas de devoir implémenter conjointement (et mettre au point) les versions CPU équivalentes des algorithmes concernés.   

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+\newlabel{lst-convo-gene3reg8}{{9.1}{112}{Kernel réalisant la convolution par un masque moyenneur 3$\times $3 dont les coefficients normalisés sont codés \textit {en dur}, dans les registres du GPU}{lstlisting.9.1}{}}
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+\newlabel{tab-convo-gene3reg8-2070}{{9.1}{113}{Performances des kernels effectuant la convolution non-séparable sur le modèle du listing \ref {lst-convo-gene3reg8}, sur GPU C2070. Le temps d'exécution correspond à la seule exécution du kernel. Le débit global intègre les temps de transfert. Les valeurs en gras correspondent au traitement de référence}{table.9.1}{}}
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+\newlabel{tab-convo-gene3reg8-480}{{9.2}{113}{Performances des kernels effectuant la convolution non-séparable sur le modèle du listing \ref {lst-convo-gene3reg8}, sur GPU GTX280. Le temps d'exécution correspond à la seule exécution du kernel. Le débit global intègre les temps de transfert. Les valeurs en gras correspondent au traitement de référence}{table.9.2}{}}
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+\newlabel{tab-convo-8x8p}{{9.3}{115}{Performances des kernels effectuant la convolution non-séparable sur le modèle du listing \ref {lst-convo-8x8pL3}, sur GPU C2070. Le temps d'exécution correspond à la seule exécution du kernel. Le débit global intègre les temps de transfert. Les valeurs en gras correspondent au traitement de référence. \relax }{table.9.3}{}}
+\newlabel{lst-convo-8x8pL3}{{9.2}{115}{Kernel réalisant la convolution par un masque 3$\times $3 dont les coefficients normalisés sont en mémoire constante}{lstlisting.9.2}{}}
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+\@writefile{toc}{\contentsline {section}{\numberline {9.4}Cas de la convolution s\IeC {\'e}parable}{117}{section.9.4}}
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+\@writefile{lol}{\contentsline {lstlisting}{\numberline {9.3}Kernel r\IeC {\'e}alisant la convolution verticale k$\times $1 avec utilisation de la m\IeC {\'e}moire partag\IeC {\'e}e.}{117}{lstlisting.9.3}}
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+\@writefile{lot}{\contentsline {table}{\numberline {9.7}{\ignorespaces Performances des kernels effectuant la convolution s\IeC {\'e}parable optimis\IeC {\'e}e sur le mod\IeC {\`e}le des listings \ref  {lst-convo-1Dv} et \ref  {lst-convons-optim}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} l'ex\IeC {\'e}cution des 2 kernels.}}{122}{table.9.7}}
+\newlabel{tab-convons-optim}{{9.7}{122}{Performances des kernels effectuant la convolution séparable optimisée sur le modèle des listings \ref {lst-convo-1Dv} et \ref {lst-convons-optim}, sur GPU C2070. Le temps d'exécution correspond à l'exécution des 2 kernels}{table.9.7}{}}
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+\newlabel{tab-convons-tpg}{{9.8}{122}{Débit global en ms (incluant les transferts) des kernels effectuant la convolution séparable sur le modèle des listings \ref {lst-convo-1Dv} et \ref {lst-convons-optim}, sur GPU C2070}{table.9.8}{}}
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 \bibcite{kddcup99}{1}
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diff --git a/THESE/these.lof b/THESE/these.lof
index 4785d1b7bd528250663f0b4badb9ab41dae6a069..f88174aba4a6f878cb9a50f69bf1e3a0487a6cfb 100644 (file)
--- a/THESE/these.lof
+++ b/THESE/these.lof
@@ -1,245 +1,246 @@
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-\contentsline {figure}{\numberline {2.2}{\ignorespaces Comparaison des performances des GPUs Nvidia et des CPU Intel (d'apr\IeC {\`e}s \cite {CUDAPG}).}}{14}{figure.2.2}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Nombre maximum th\IeC {\'e}orique d'op\IeC {\'e}rations en virgule flottante par seconde en fonction de l'ann\IeC {\'e}e et de l'architecture.}}}{14}{figure.2.2}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Bande passante th\IeC {\'e}orique maximale des diverses architectures.}}}{14}{figure.2.2}
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+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {D\IeC {\'e}tail des op\IeC {\'e}rations effectu\IeC {\'e}es par le \textit {kernel} \texttt {add\_sums2prefixes()}.}}}{69}{figure.6.7}
+\contentsline {figure}{\numberline {6.8}{\ignorespaces Structuration des donn\IeC {\'e}es en m\IeC {\'e}moire du GPU pour l'\IeC {\'e}valuation en parall\IeC {\`e}le de l'ensemble des \IeC {\'e}volutions possibles du contour.}}{71}{figure.6.8}
+\contentsline {figure}{\numberline {6.9}{\ignorespaces Comparaison des cycles de d\IeC {\'e}placement des n\oe uds. Ligne du haut : version s\IeC {\'e}quentielle. Ligne du bas : version parall\IeC {\`e}le. Les segments en rouge sont des segments du contour non \IeC {\'e}valu\IeC {\'e}s, alors que ceux en pointill\IeC {\'e}s sont les paires ayant re\IeC {\c c}u les meilleures \IeC {\'e}valuations parmi les 8 d\IeC {\'e}placements possibles des n\oe uds correspondant.}}{72}{figure.6.9}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Contour de r\IeC {\'e}f\IeC {\'e}rence.}}}{72}{figure.6.9}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {D\IeC {\'e}placement du n\oe ud $N_1$. Le crit\IeC {\`e}re est am\IeC {\'e}lior\IeC {\'e}.}}}{72}{figure.6.9}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {D\IeC {\'e}placement du n\oe ud $N_2$. Le crit\IeC {\`e}re est am\IeC {\'e}lior\IeC {\'e}.}}}{72}{figure.6.9}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {D\IeC {\'e}placement en parall\IeC {\`e}le de tous les n\oe uds. Les segments du contour n'ont pas \IeC {\'e}t\IeC {\'e} valid\IeC {\'e}s. On doit recalculer le crit\IeC {\`e}re apr\IeC {\`e}s les d\IeC {\'e}placements pour savoir s'il a \IeC {\'e}t\IeC {\'e} am\IeC {\'e}lior\IeC {\'e}.}}}{72}{figure.6.9}
+\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {D\IeC {\'e}placement en parall\IeC {\`e}le des n\oe uds impairs. Le crit\IeC {\`e}re est am\IeC {\'e}lior\IeC {\'e}.}}}{72}{figure.6.9}
+\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {D\IeC {\'e}placement en parall\IeC {\`e}le des n\oe uds pairs. Un seul segment n'a pas \IeC {\'e}t\IeC {\'e} \IeC {\'e}valu\IeC {\'e}.}}}{72}{figure.6.9}
+\contentsline {figure}{\numberline {6.10}{\ignorespaces D\IeC {\'e}termination des coefficients $C(i,j)$ des pixels du contour.}}{74}{figure.6.10}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Quadrants 1 et 4}}}{74}{figure.6.10}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Quadrants 2 et 3}}}{74}{figure.6.10}
+\contentsline {figure}{\numberline {6.11}{\ignorespaces Segmentations d'une image de 100~MP en 0,59~s pour 5 it\IeC {\'e}rations. Le contour initial conserve les proportions de celui de la figure \ref {fig-snakecpu-cochon512}. }}{75}{figure.6.11}
+\contentsline {figure}{\numberline {6.12}{\ignorespaces D\IeC {\'e}termination intelligente du contour initial en deux phases successives. (a) La premi\IeC {\`e}re \IeC {\'e}tape repose sur un \IeC {\'e}chantillonnage horizontal. (b) La seconde \IeC {\'e}tape repose sur un \IeC {\'e}chantillonnage vertical.}}{77}{figure.6.12}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {D\IeC {\'e}termination de $j_L$ et $j_H$.}}}{77}{figure.6.12}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {D\IeC {\'e}termination de $i_L$ et $i_H$.}}}{77}{figure.6.12}
 \addvspace {10\p@ }
-\contentsline {figure}{\numberline {7.1}{\ignorespaces D\IeC {\'e}tail des motifs et de leur repr\IeC {\'e}sentation interne, pour la taille $a=5$. }}{82}{figure.7.1}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Les 8 premi\IeC {\`e}res lignes de la table $P_5$. Les \IeC {\'e}l\IeC {\'e}ments sont les positions relatives des pixels de chaque motif par rapport au pixel central.}}}{82}{figure.7.1}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Motifs des 8 premiers segments associ\IeC {\'e}s aux 8 premi\IeC {\`e}res lignes de $P_5$. Les pixels noirs repr\IeC {\'e}sentent le pixel trait\IeC {\'e} (ou pixel central), qui n'appartient pas au motif. Les pixels gris sont ceux qui constituent le motif.}}}{82}{figure.7.1}
-\contentsline {figure}{\numberline {7.2}{\ignorespaces Exemple de la r\IeC {\'e}partition des pixels dans la r\IeC {\'e}gion $\omega $ pour le calcul de la vraisemblance, pour $n=6$ ($a=5$).}}{83}{figure.7.2}
-\contentsline {figure}{\numberline {7.3}{\ignorespaces Allongement du segment $S^n$. Deux candidats $S^{p'}$ et $S^{p''}$ sont \IeC {\'e}valu\IeC {\'e}s au travers du crit\IeC {\`e}re GLRT de l'\IeC {\'e}quation \textup {\hbox {\mathsurround \z@ \normalfont (\ignorespaces \ref {GLRT}\unskip \@@italiccorr )}} que seul $S^{p''}$ s'av\IeC {\`e}re satisfaire. a) Repr\IeC {\'e}sentation dans le plan de l'image. b) \IeC {\'E}volution des niveaux de gris en fonction de la position des pixels dans les lignes bris\IeC {\'e}es ainsi form\IeC {\'e}es.}}{84}{figure.7.3}
-\contentsline {figure}{\numberline {7.4}{\ignorespaces Processus de s\IeC {\'e}lection lors de l'allongement d'une isoline comportant initialement deux segments $s_1$ et $s_2$. Dans cet exemple $a=5$ et $\Delta d_{max}=2$. Chaque segment \IeC {\'e}valu\IeC {\'e} est soumis au crit\IeC {\`e}re GLRT. Si au moins un des segments pr\IeC {\'e}sente un test GLRT positif, alors l'allongement est r\IeC {\'e}alis\IeC {\'e} avec le segment qui forme l'isoline la plus vraisemblable.}}{86}{figure.7.4}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Isoline comprenant deux segments $s_1$ et $s_2$.}}}{86}{figure.7.4}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Premier segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,0}$.}}}{86}{figure.7.4}
-\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Deuxi\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,1}$.}}}{86}{figure.7.4}
-\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Troisi\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,2}$.}}}{86}{figure.7.4}
-\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Quatri\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,3}$.}}}{86}{figure.7.4}
-\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {Cinqui\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,4}$.}}}{86}{figure.7.4}
-\contentsline {figure}{\numberline {7.5}{\ignorespaces Images non bruit\IeC {\'e}es de la base d'images en niveaux de gris de S. Lansel.}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {airplane}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {boat}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {barbara}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {couple}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {elaine}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {fingerprint}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(g)}{\ignorespaces {goldhill}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(h)}{\ignorespaces {lena}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(i)}{\ignorespaces {man}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(j)}{\ignorespaces {mandrill}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(k)}{\ignorespaces {peppers}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(l)}{\ignorespaces {stream}}}{87}{figure.7.5}
-\contentsline {subfigure}{\numberline {(m)}{\ignorespaces {zelda}}}{87}{figure.7.5}
-\contentsline {figure}{\numberline {7.6}{\ignorespaces Histogramme des \IeC {\'e}carts angulaires entre la direction primaire de l'isoline optimale et celle du segment s\IeC {\'e}lectionn\IeC {\'e} par PI-LD avec $q=1$ (sans allongement), pour l'image du singe (Mandrill). Pour la tr\IeC {\`e}s grande majorit\IeC {\'e} des pixels, l'\IeC {\'e}cart est nul.}}{87}{figure.7.6}
-\contentsline {figure}{\numberline {7.7}{\ignorespaces Histogrammes des \IeC {\'e}carts angulaires entre la direction primaire de l'isoline optimale et celle de l'isoline s\IeC {\'e}lectionn\IeC {\'e}e, pour les images de l'ensemble de test de S. Lansel. La r\IeC {\'e}partition des erreurs est semblable dans toutes ces images, mais \IeC {\'e}galement dans toute image naturelle.}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Airplane}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Barbara}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Boat}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Couple}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Elaine}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {Finger}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(g)}{\ignorespaces {Goldhill}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(h)}{\ignorespaces {Lena}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(i)}{\ignorespaces {Man}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(j)}{\ignorespaces {Peppers}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(k)}{\ignorespaces {Stream}}}{88}{figure.7.7}
-\contentsline {subfigure}{\numberline {(l)}{\ignorespaces {Zelda}}}{88}{figure.7.7}
-\contentsline {figure}{\numberline {7.8}{\ignorespaces Exemple d'application du proc\IeC {\'e}d\IeC {\'e} d'allongement \IeC {\`a} une isoline comprenant initialement 2 segments. la longueur des segments est $a=5$. Le proc\IeC {\'e}d\IeC {\'e} se r\IeC {\'e}p\IeC {\`e}te jusqu'\IeC {\`a} ce que le test GLRT \IeC {\'e}choue.}}{89}{figure.7.8}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Isoline avec 2 segments $s_1$ et $s_2$ d\IeC {\'e}j\IeC {\`a} valid\IeC {\'e}s.}}}{89}{figure.7.8}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {La direction de $s_3$ est l'\IeC {\'e}l\IeC {\'e}ment $(i_2,j_2)$ de $I_{\Theta }$.}}}{89}{figure.7.8}
-\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Le motif de $s_3$ est lu dans $p_5$ et appliqu\IeC {\'e} en $(i_2,j_2)$. $C_x$ et $C_{x^2}$ sont donn\IeC {\'e}es par $I_{\Sigma }(i_2,j_2)$ et le test GLRT est effectu\IeC {\'e}.}}}{89}{figure.7.8}
-\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Si l'allongement est valid\IeC {\'e}, $s_3$ est d\IeC {\'e}finitivement int\IeC {\'e}gr\IeC {\'e}.}}}{89}{figure.7.8}
-\contentsline {figure}{\numberline {7.9}{\ignorespaces Situation de la r\IeC {\'e}gion servant \IeC {\`a} illustrer le comportant du mod\IeC {\`e}le PI-PD dans les zones \IeC {\`a} faible pente (LSR).}}{90}{figure.7.9}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image de r\IeC {\'e}f\IeC {\'e}rence non bruit\IeC {\'e}e.}}}{90}{figure.7.9}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {La r\IeC {\'e}gion de 11$\times $11 pixels \IeC {\'e}tudi\IeC {\'e}e.}}}{90}{figure.7.9}
-\contentsline {figure}{\numberline {7.10}{\ignorespaces Comportement du mod\IeC {\`e}le PI-PD dans les zones de faible et \IeC {\`a} forte pente. On constate un manque de robustesse dans les zones \IeC {\`a} faible pente : les directions ne sont pas reproduites d'un tirage \IeC {\`a} l'autre, contrairement \IeC {\`a} celles de la zone de transition.}}{93}{figure.7.10}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image de r\IeC {\'e}f\IeC {\'e}rence.}}}{93}{figure.7.10}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Image corrompue par le tirage de bruit $n^{\circ }1$}}}{93}{figure.7.10}
-\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Image corrompue par le tirage de bruit $n^{\circ }2$}}}{93}{figure.7.10}
-\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Directions d\IeC {\'e}termin\IeC {\'e}es par le PI-PD pour le tirage $n^{\circ }1$}}}{93}{figure.7.10}
-\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Directions d\IeC {\'e}termin\IeC {\'e}e par le PI-PD pour le tirage $n^{\circ }2$}}}{93}{figure.7.10}
-\contentsline {figure}{\numberline {7.11}{\ignorespaces Motif de d\IeC {\'e}tection des zones \IeC {\`a} faible pente, pour le cas $\Theta =\Theta _4=45^{\circ }$. L'\IeC {\'e}l\IeC {\'e}vation des pixels permet juste de les distinguer selon 3 classes : l'\IeC {\'e}l\IeC {\'e}vation 1 est associ\IeC {\'e}e aux pixels de la r\IeC {\'e}gion $T$, l'\IeC {\'e}l\IeC {\'e}vation 0.5 est associ\IeC {\'e}e \IeC {\`a} ceux de la r\IeC {\'e}gion $B$ et l'\IeC {\'e}l\IeC {\'e}vation 0 d\IeC {\'e}signe les pixels n'intervenant pas dans la d\IeC {\'e}tection.}}{94}{figure.7.11}
-\contentsline {figure}{\numberline {7.12}{\ignorespaces Classification des pixels d'une image bruit\IeC {\'e}e, pour une valeur de seuil $T2=2$ du d\IeC {\'e}tecteur. (b) Les pixels en noir sont ceux \IeC {\`a} qui le PI-PD sera appliqu\IeC {\'e}. Les pixels en blancs se verront appliquer une moyenne sur tout ou partie du voisinage.}}{94}{figure.7.12}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image bruit\IeC {\'e}e}}}{94}{figure.7.12}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Classification des pixels. }}}{94}{figure.7.12}
-\contentsline {figure}{\numberline {7.13}{\ignorespaces Comparaison des rendus des traitements compar\IeC {\'e}s. Rang\IeC {\'e}e du haut : les images compl\IeC {\`e}tes. Rang\IeC {\'e}e du bas : Zooms sur une zone de l'\IeC {\'\i }mage au dessus.}}{95}{figure.7.13}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image \textit {airplane} bruit\IeC {\'e}e.}}}{95}{figure.7.13}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Image \textit {airplane} filtr\IeC {\'e}e par moyenneur 5$\times $5.}}}{95}{figure.7.13}
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-\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Image \textit {airplane} filtr\IeC {\'e}e par BM3D.}}}{95}{figure.7.13}
-\contentsline {figure}{\numberline {7.14}{\ignorespaces Images non bruit\IeC {\'e}es de la base tid2008.}}{98}{figure.7.14}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {I01}}}{98}{figure.7.14}
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-\contentsline {subfigure}{\numberline {(u)}{\ignorespaces {I21}}}{98}{figure.7.14}
-\contentsline {subfigure}{\numberline {(v)}{\ignorespaces {I22}}}{98}{figure.7.14}
-\contentsline {subfigure}{\numberline {(w)}{\ignorespaces {I23}}}{98}{figure.7.14}
-\contentsline {subfigure}{\numberline {(x)}{\ignorespaces {I24}}}{98}{figure.7.14}
-\contentsline {subfigure}{\numberline {(y)}{\ignorespaces {I25}}}{98}{figure.7.14}
-\contentsline {figure}{\numberline {7.15}{\ignorespaces Exemples de r\IeC {\'e}sultat de traitement par PI-PD RVB et par CBM3D pour deux images de la base tid2008 (une image naturelle et l'image de synth\IeC {\`e}se). Il peut \IeC {\^e}tre n\IeC {\'e}cessaire de zoomer sur le document num\IeC {\'e}rique pour visualiser les d\IeC {\'e}tails.}}{99}{figure.7.15}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image I09 bruit\IeC {\'e}e, PSNR-HVS-M=23,70~dB.}}}{99}{figure.7.15}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Image I09 filtr\IeC {\'e}e par PI-PD, PSNR-HVS-M=27,62~dB.}}}{99}{figure.7.15}
-\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Image I09 filtr\IeC {\'e}e par CBM3D, PSNR-HVS-M=33,26~dB.}}}{99}{figure.7.15}
-\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Image I25 bruit\IeC {\'e}e, PSNR-HVS-M=24,46~dB.}}}{99}{figure.7.15}
-\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Image I25 filtr\IeC {\'e}e par PI-PD, PSNR-HVS-M=24,62~dB.}}}{99}{figure.7.15}
-\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {Image I25 filtr\IeC {\'e}e par CBM3D, PSNR-HVS-M=31,09~dB.}}}{99}{figure.7.15}
+\contentsline {figure}{\numberline {7.1}{\ignorespaces D\IeC {\'e}tail des motifs et de leur repr\IeC {\'e}sentation interne, pour la taille $a=5$. }}{80}{figure.7.1}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Les 8 premi\IeC {\`e}res lignes de la table $P_5$. Les \IeC {\'e}l\IeC {\'e}ments sont les positions relatives des pixels de chaque motif par rapport au pixel central.}}}{80}{figure.7.1}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Motifs des 8 premiers segments associ\IeC {\'e}s aux 8 premi\IeC {\`e}res lignes de $P_5$. Les pixels noirs repr\IeC {\'e}sentent le pixel trait\IeC {\'e} (ou pixel central), qui n'appartient pas au motif. Les pixels gris sont ceux qui constituent le motif.}}}{80}{figure.7.1}
+\contentsline {figure}{\numberline {7.2}{\ignorespaces Exemple de la r\IeC {\'e}partition des pixels dans la r\IeC {\'e}gion $\omega $ pour le calcul de la vraisemblance, pour $n=6$ ($a=5$).}}{81}{figure.7.2}
+\contentsline {figure}{\numberline {7.3}{\ignorespaces Allongement du segment $S^n$. Deux candidats $S^{p'}$ et $S^{p''}$ sont \IeC {\'e}valu\IeC {\'e}s au travers du crit\IeC {\`e}re GLRT de l'\IeC {\'e}quation \textsuperscript {\hbox {\mathsurround \z@ \normalfont (\ignorespaces \ref {GLRT}\unskip \@@italiccorr )}}\xspace que seul $S^{p''}$ s'av\IeC {\`e}re satisfaire. a) Repr\IeC {\'e}sentation dans le plan de l'image. b) \IeC {\'E}volution des niveaux de gris en fonction de la position des pixels dans les lignes bris\IeC {\'e}es ainsi form\IeC {\'e}es.}}{82}{figure.7.3}
+\contentsline {figure}{\numberline {7.4}{\ignorespaces Processus de s\IeC {\'e}lection lors de l'allongement d'une isoline comportant initialement deux segments $s_1$ et $s_2$. Dans cet exemple $a=5$ et $\Delta d_{max}=2$. Chaque segment \IeC {\'e}valu\IeC {\'e} est soumis au crit\IeC {\`e}re GLRT. Si au moins un des segments pr\IeC {\'e}sente un test GLRT positif, alors l'allongement est r\IeC {\'e}alis\IeC {\'e} avec le segment qui forme l'isoline la plus vraisemblable.}}{84}{figure.7.4}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Isoline comprenant deux segments $s_1$ et $s_2$.}}}{84}{figure.7.4}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Premier segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,0}$.}}}{84}{figure.7.4}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Deuxi\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,1}$.}}}{84}{figure.7.4}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Troisi\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,2}$.}}}{84}{figure.7.4}
+\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Quatri\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,3}$.}}}{84}{figure.7.4}
+\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {Cinqui\IeC {\`e}me segment \IeC {\'e}valu\IeC {\'e}, associ\IeC {\'e} au motif $p_{5,4}$.}}}{84}{figure.7.4}
+\contentsline {figure}{\numberline {7.5}{\ignorespaces Images non bruit\IeC {\'e}es de la base d'images en niveaux de gris de S. Lansel.}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {airplane}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {boat}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {barbara}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {couple}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {elaine}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {fingerprint}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(g)}{\ignorespaces {goldhill}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(h)}{\ignorespaces {lena}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(i)}{\ignorespaces {man}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(j)}{\ignorespaces {mandrill}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(k)}{\ignorespaces {peppers}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(l)}{\ignorespaces {stream}}}{85}{figure.7.5}
+\contentsline {subfigure}{\numberline {(m)}{\ignorespaces {zelda}}}{85}{figure.7.5}
+\contentsline {figure}{\numberline {7.6}{\ignorespaces Histogramme des \IeC {\'e}carts angulaires entre la direction primaire de l'isoline optimale et celle du segment s\IeC {\'e}lectionn\IeC {\'e} par PI-LD avec $q=1$ (sans allongement), pour l'image du singe (Mandrill). Pour la tr\IeC {\`e}s grande majorit\IeC {\'e} des pixels, l'\IeC {\'e}cart est nul.}}{85}{figure.7.6}
+\contentsline {figure}{\numberline {7.7}{\ignorespaces Histogrammes des \IeC {\'e}carts angulaires entre la direction primaire de l'isoline optimale et celle de l'isoline s\IeC {\'e}lectionn\IeC {\'e}e, pour les images de l'ensemble de test de S. Lansel. La r\IeC {\'e}partition des erreurs est semblable dans toutes ces images, mais \IeC {\'e}galement dans toute image naturelle.}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Airplane}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Barbara}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Boat}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Couple}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Elaine}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {Finger}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(g)}{\ignorespaces {Goldhill}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(h)}{\ignorespaces {Lena}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(i)}{\ignorespaces {Man}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(j)}{\ignorespaces {Peppers}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(k)}{\ignorespaces {Stream}}}{86}{figure.7.7}
+\contentsline {subfigure}{\numberline {(l)}{\ignorespaces {Zelda}}}{86}{figure.7.7}
+\contentsline {figure}{\numberline {7.8}{\ignorespaces Exemple d'application du proc\IeC {\'e}d\IeC {\'e} d'allongement \IeC {\`a} une isoline comprenant initialement 2 segments. la longueur des segments est $a=5$. Le proc\IeC {\'e}d\IeC {\'e} se r\IeC {\'e}p\IeC {\`e}te jusqu'\IeC {\`a} ce que le test GLRT \IeC {\'e}choue.}}{87}{figure.7.8}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Isoline avec 2 segments $s_1$ et $s_2$ d\IeC {\'e}j\IeC {\`a} valid\IeC {\'e}s.}}}{87}{figure.7.8}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {La direction de $s_3$ est l'\IeC {\'e}l\IeC {\'e}ment $(i_2,j_2)$ de $I_{\Theta }$.}}}{87}{figure.7.8}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Le motif de $s_3$ est lu dans $p_5$ et appliqu\IeC {\'e} en $(i_2,j_2)$. $C_x$ et $C_{x^2}$ sont donn\IeC {\'e}es par $I_{\Sigma }(i_2,j_2)$ et le test GLRT est effectu\IeC {\'e}.}}}{87}{figure.7.8}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Si l'allongement est valid\IeC {\'e}, $s_3$ est d\IeC {\'e}finitivement int\IeC {\'e}gr\IeC {\'e}.}}}{87}{figure.7.8}
+\contentsline {figure}{\numberline {7.9}{\ignorespaces Situation de la r\IeC {\'e}gion servant \IeC {\`a} illustrer le comportant du mod\IeC {\`e}le PI-PD dans les zones \IeC {\`a} faible pente (LSR).}}{88}{figure.7.9}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image de r\IeC {\'e}f\IeC {\'e}rence non bruit\IeC {\'e}e.}}}{88}{figure.7.9}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {La r\IeC {\'e}gion de 11$\times $11 pixels \IeC {\'e}tudi\IeC {\'e}e.}}}{88}{figure.7.9}
+\contentsline {figure}{\numberline {7.10}{\ignorespaces Comportement du mod\IeC {\`e}le PI-PD dans les zones de faible et \IeC {\`a} forte pente. On constate un manque de robustesse dans les zones \IeC {\`a} faible pente : les directions ne sont pas reproduites d'un tirage \IeC {\`a} l'autre, contrairement \IeC {\`a} celles de la zone de transition.}}{91}{figure.7.10}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image de r\IeC {\'e}f\IeC {\'e}rence.}}}{91}{figure.7.10}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Image corrompue par le tirage de bruit $n^{\circ }1$}}}{91}{figure.7.10}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Image corrompue par le tirage de bruit $n^{\circ }2$}}}{91}{figure.7.10}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Directions d\IeC {\'e}termin\IeC {\'e}es par le PI-PD pour le tirage $n^{\circ }1$}}}{91}{figure.7.10}
+\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Directions d\IeC {\'e}termin\IeC {\'e}e par le PI-PD pour le tirage $n^{\circ }2$}}}{91}{figure.7.10}
+\contentsline {figure}{\numberline {7.11}{\ignorespaces Motif de d\IeC {\'e}tection des zones \IeC {\`a} faible pente, pour le cas $\Theta =\Theta _4=45^{\circ }$. L'\IeC {\'e}l\IeC {\'e}vation des pixels permet juste de les distinguer selon 3 classes : l'\IeC {\'e}l\IeC {\'e}vation 1 est associ\IeC {\'e}e aux pixels de la r\IeC {\'e}gion $T$, l'\IeC {\'e}l\IeC {\'e}vation 0.5 est associ\IeC {\'e}e \IeC {\`a} ceux de la r\IeC {\'e}gion $B$ et l'\IeC {\'e}l\IeC {\'e}vation 0 d\IeC {\'e}signe les pixels n'intervenant pas dans la d\IeC {\'e}tection.}}{92}{figure.7.11}
+\contentsline {figure}{\numberline {7.12}{\ignorespaces Classification des pixels d'une image bruit\IeC {\'e}e, pour une valeur de seuil $T2=2$ du d\IeC {\'e}tecteur. (b) Les pixels en noir sont ceux \IeC {\`a} qui le PI-PD sera appliqu\IeC {\'e}. Les pixels en blancs se verront appliquer une moyenne sur tout ou partie du voisinage.}}{92}{figure.7.12}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image bruit\IeC {\'e}e}}}{92}{figure.7.12}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Classification des pixels. }}}{92}{figure.7.12}
+\contentsline {figure}{\numberline {7.13}{\ignorespaces Comparaison des rendus des traitements compar\IeC {\'e}s. Rang\IeC {\'e}e du haut : les images compl\IeC {\`e}tes. Rang\IeC {\'e}e du bas : Zooms sur une zone de l'\IeC {\'\i }mage au dessus.}}{93}{figure.7.13}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image \textit {airplane} bruit\IeC {\'e}e.}}}{93}{figure.7.13}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Image \textit {airplane} filtr\IeC {\'e}e par moyenneur 5$\times $5.}}}{93}{figure.7.13}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Image \textit {airplane} filtr\IeC {\'e}e par PI-PD hybride avec $a=5$, $q=5$, $T_{max}=2$ et $T2_{max}=2$.}}}{93}{figure.7.13}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Image \textit {airplane} filtr\IeC {\'e}e par BM3D.}}}{93}{figure.7.13}
+\contentsline {figure}{\numberline {7.14}{\ignorespaces Images non bruit\IeC {\'e}es de la base tid2008.}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {I01}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {I02}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {I03}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {I04}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {I05}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {I06}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(g)}{\ignorespaces {I07}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(h)}{\ignorespaces {I08}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(i)}{\ignorespaces {I09}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(j)}{\ignorespaces {I10}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(k)}{\ignorespaces {I11}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(l)}{\ignorespaces {I12}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(m)}{\ignorespaces {I13}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(n)}{\ignorespaces {I14}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(o)}{\ignorespaces {I15}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(p)}{\ignorespaces {I16}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(q)}{\ignorespaces {I17}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(r)}{\ignorespaces {I18}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(s)}{\ignorespaces {I19}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(t)}{\ignorespaces {I20}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(u)}{\ignorespaces {I21}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(v)}{\ignorespaces {I22}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(w)}{\ignorespaces {I23}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(x)}{\ignorespaces {I24}}}{96}{figure.7.14}
+\contentsline {subfigure}{\numberline {(y)}{\ignorespaces {I25}}}{96}{figure.7.14}
+\contentsline {figure}{\numberline {7.15}{\ignorespaces Exemples de r\IeC {\'e}sultat de traitement par PI-PD RVB et par CBM3D pour deux images de la base tid2008 (une image naturelle et l'image de synth\IeC {\`e}se). Il peut \IeC {\^e}tre n\IeC {\'e}cessaire de zoomer sur le document num\IeC {\'e}rique pour visualiser les d\IeC {\'e}tails.}}{97}{figure.7.15}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Image I09 bruit\IeC {\'e}e, PSNR-HVS-M=23,70~dB.}}}{97}{figure.7.15}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Image I09 filtr\IeC {\'e}e par PI-PD, PSNR-HVS-M=27,62~dB.}}}{97}{figure.7.15}
+\contentsline {subfigure}{\numberline {(c)}{\ignorespaces {Image I09 filtr\IeC {\'e}e par CBM3D, PSNR-HVS-M=33,26~dB.}}}{97}{figure.7.15}
+\contentsline {subfigure}{\numberline {(d)}{\ignorespaces {Image I25 bruit\IeC {\'e}e, PSNR-HVS-M=24,46~dB.}}}{97}{figure.7.15}
+\contentsline {subfigure}{\numberline {(e)}{\ignorespaces {Image I25 filtr\IeC {\'e}e par PI-PD, PSNR-HVS-M=24,62~dB.}}}{97}{figure.7.15}
+\contentsline {subfigure}{\numberline {(f)}{\ignorespaces {Image I25 filtr\IeC {\'e}e par CBM3D, PSNR-HVS-M=31,09~dB.}}}{97}{figure.7.15}
 \addvspace {10\p@ }
-\contentsline {figure}{\numberline {8.1}{\ignorespaces Application de la s\IeC {\'e}lection de m\IeC {\'e}diane par oubli \IeC {\`a} une fen\IeC {\^e}tre de $3\times 3$ pixels. }}{106}{figure.8.1}
-\contentsline {figure}{\numberline {8.2}{\ignorespaces Premi\IeC {\`e}re \IeC {\'e}tape d'identification des extrema pour un filtre 5$\times $5, avec maximisation de l'ILP (Instruction Level Parallelism) pour l'identification des extrema.}}{107}{figure.8.2}
-\contentsline {figure}{\numberline {8.3}{\ignorespaces Gestion des \IeC {\'e}l\IeC {\'e}ments communs aux fen\IeC {\^e}tres de deux pixels centraux voisins dans un filtre m\IeC {\'e}dian 5$\times $5. La liste initiale comprend les 14 premiers \IeC {\'e}l\IeC {\'e}ments communs, puis les 7 premi\IeC {\`e}res \IeC {\'e}tapes de s\IeC {\'e}lection sont conduites en commun avant que les 5 derni\IeC {\`e}res le soient en parall\IeC {\`e}le, mais de mani\IeC {\`e}re disjointe.}}{108}{figure.8.3}
-\contentsline {figure}{\numberline {8.4}{\ignorespaces Comparaison des d\IeC {\'e}bits (MP/s) atteints par notre impl\IeC {\'e}mentation not\IeC {\'e}e PRMF, avec les principales solutions de r\IeC {\'e}f\IeC {\'e}rence. De gauche \IeC {\`a} droite : PCMF, BVM, PRMF, ArrayFire (impossible en 4096$\times $4096)}}{110}{figure.8.4}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {image 512$\times $512 pixels.}}}{110}{figure.8.4}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {image 4096$\times $4096 pixels.}}}{110}{figure.8.4}
+\contentsline {figure}{\numberline {8.1}{\ignorespaces Application de la s\IeC {\'e}lection de m\IeC {\'e}diane par oubli \IeC {\`a} une fen\IeC {\^e}tre de $3\times 3$ pixels. }}{105}{figure.8.1}
+\contentsline {figure}{\numberline {8.2}{\ignorespaces Premi\IeC {\`e}re \IeC {\'e}tape d'identification des extrema pour un filtre 5$\times $5, avec maximisation de l'ILP (Instruction Level Parallelism) pour l'identification des extrema.}}{105}{figure.8.2}
+\contentsline {figure}{\numberline {8.3}{\ignorespaces Gestion des \IeC {\'e}l\IeC {\'e}ments communs aux fen\IeC {\^e}tres de deux pixels centraux voisins dans un filtre m\IeC {\'e}dian 5$\times $5. La liste initiale comprend les 14 premiers \IeC {\'e}l\IeC {\'e}ments communs, puis les 7 premi\IeC {\`e}res \IeC {\'e}tapes de s\IeC {\'e}lection sont conduites en commun avant que les 5 derni\IeC {\`e}res le soient en parall\IeC {\`e}le, mais de mani\IeC {\`e}re disjointe.}}{106}{figure.8.3}
+\contentsline {figure}{\numberline {8.4}{\ignorespaces Comparaison des d\IeC {\'e}bits (MP/s) atteints par notre impl\IeC {\'e}mentation not\IeC {\'e}e PRMF, avec les principales solutions de r\IeC {\'e}f\IeC {\'e}rence. De gauche \IeC {\`a} droite : PCMF, BVM, PRMF, ArrayFire (impossible en 4096$\times $4096)}}{108}{figure.8.4}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {image 512$\times $512 pixels.}}}{108}{figure.8.4}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {image 4096$\times $4096 pixels.}}}{108}{figure.8.4}
+\addvspace {10\p@ }
+\contentsline {figure}{\numberline {9.1}{\ignorespaces Multiplicit\IeC {\'e} des implications des pixels de la zone d'int\IeC {\'e}r\IeC {\^e}t d'un thread dans les calculs de convolution. Le nombre de calculs dans lequel est impliqu\IeC {\'e} un pixel est inscrit en son centre. Le premier pixel du paquet, ou pixel de base, est rep\IeC {\'e}r\IeC {\'e} par ses coordonn\IeC {\'e}es $(x, y)$ ; le dernier a pour coordonn\IeC {\'e}es $(x+7,y)$}}{114}{figure.9.1}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Cas d'un masque de taille 3$\times $3 ($k=1$) o\IeC {\`u} l'on d\IeC {\'e}nombre 6 colonnes centrales, soit 18 pixels de multiplicit\IeC {\'e} maximale 3.}}}{114}{figure.9.1}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Cas d'un masque de taille 5$\times $5 ($k=2$) o\IeC {\`u} l'on d\IeC {\'e}nombre 4 colonnes centrales, soit 20 pixels de multiplicit\IeC {\'e} maximale 5.}}}{114}{figure.9.1}
+\contentsline {figure}{\numberline {9.2}{\ignorespaces G\IeC {\'e}n\IeC {\'e}rateur de codes sources pour les filtres GPU rapides.}}{123}{figure.9.2}
+\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {S\IeC {\'e}lection des param\IeC {\`e}tres.}}}{123}{figure.9.2}
+\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {T\IeC {\'e}l\IeC {\'e}chargement des fichiers.}}}{123}{figure.9.2}
 \addvspace {10\p@ }
-\contentsline {figure}{\numberline {9.1}{\ignorespaces Multiplicit\IeC {\'e} des implications des pixels de la zone d'int\IeC {\'e}r\IeC {\^e}t d'un thread dans les calculs de convolution. Le nombre de calculs dans lequel est impliqu\IeC {\'e} un pixel est inscrit en son centre. Le premier pixel du paquet, ou pixel de base, est rep\IeC {\'e}r\IeC {\'e} par ses coordonn\IeC {\'e}es $(x, y)$ ; le dernier a pour coordonn\IeC {\'e}es $(x+7,y)$}}{116}{figure.9.1}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Cas d'un masque de taille 3$\times $3 ($k=1$) o\IeC {\`u} l'on d\IeC {\'e}nombre 6 colonnes centrales, soit 18 pixels de multiplicit\IeC {\'e} maximale 3.}}}{116}{figure.9.1}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Cas d'un masque de taille 5$\times $5 ($k=2$) o\IeC {\`u} l'on d\IeC {\'e}nombre 4 colonnes centrales, soit 20 pixels de multiplicit\IeC {\'e} maximale 5.}}}{116}{figure.9.1}
-\contentsline {figure}{\numberline {9.2}{\ignorespaces G\IeC {\'e}n\IeC {\'e}rateur de codes sources pour les filtres GPU rapides.}}{125}{figure.9.2}
-\contentsline {subfigure}{\numberline {(a)}{\ignorespaces {S\IeC {\'e}lection des param\IeC {\`e}tres.}}}{125}{figure.9.2}
-\contentsline {subfigure}{\numberline {(b)}{\ignorespaces {T\IeC {\'e}l\IeC {\'e}chargement des fichiers.}}}{125}{figure.9.2}
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diff --git a/THESE/these.lot b/THESE/these.lot
index feae2c10782a327ab0da01735e50ddf38fd794e1..62c2c51da26783284178279fccd3dcbe864973e5 100644 (file)
--- a/THESE/these.lot
+++ b/THESE/these.lot
@@ -6,26 +6,27 @@
 \addvspace {10\p@ }
 \addvspace {10\p@ }
 \addvspace {10\p@ }
-\contentsline {table}{\numberline {6.1}{\ignorespaces Valeur du coefficient $C(i,j)$ en fonction des valeurs des codes de Freeman des vecteurs $f_{in}$ et $f_{out}$ \cite {GallandBR03}.}}{64}{table.6.1}
+\contentsline {table}{\numberline {6.1}{\ignorespaces Valeur du coefficient $C(i,j)$ en fonction des valeurs des codes de Freeman des vecteurs $f_{in}$ et $f_{out}$ \cite {GallandBR03}.}}{62}{table.6.1}
 \contentsline {table}{\numberline {6.2}{\ignorespaces Performances (en secondes) de la segmentation par snake polygonal sur CPU en fonction de la taille de l'image \IeC {\`a} traiter. Les temps sont obtenus avec la m\IeC {\^e}me image de test dilat\IeC {\'e}e et bruit\IeC {\'e}e et un contour initial carr\IeC {\'e} dont la distance aux bords est proportionnelle \IeC {\`a} la taille de l'image. Seule l'image en 15~MP a pu \IeC {\^e}tre trait\IeC {\'e}e par une impl\IeC {\'e}mentation utilisant SSE2.}}{67}{table.6.2}
 \contentsline {table}{\numberline {6.3}{\ignorespaces Acc\IeC {\'e}l\IeC {\'e}ration constat\IeC {\'e}e, pour le calcul des images cumul\IeC {\'e}es, de l'impl\IeC {\'e}mentation GPU (C2070) par rapport \IeC {\`a} l'impl\IeC {\'e}mentation CPU de r\IeC {\'e}f\IeC {\'e}rence.}}{70}{table.6.3}
-\contentsline {table}{\numberline {6.4}{\ignorespaces Comparaison des temps d'ex\IeC {\'e}cution de l'impl\IeC {\'e}mentation GPU (C2070) par rapport \IeC {\`a} l'impl\IeC {\'e}mentation CPU de r\IeC {\'e}f\IeC {\'e}rence, appliqu\IeC {\'e}s \IeC {\`a} une m\IeC {\^e}me image dilat\IeC {\'e}e (fig. \ref {fig-snakecpu-cochon512}) pour en adapter la taille.}}{77}{table.6.4}
-\addvspace {10\p@ }
-\contentsline {table}{\numberline {7.1}{\ignorespaces Temps de calcul et de transfert des impl\IeC {\'e}mentations compar\IeC {\'e}es. }}{100}{table.7.1}
-\contentsline {table}{\numberline {7.2}{\ignorespaces Comparaison image par image de la qualit\IeC {\'e} de d\IeC {\'e}bruitage des filtres PI-LD et PI-PD hybride propos\IeC {\'e} par rapport \IeC {\`a} BM3D pris comme r\IeC {\'e}f\IeC {\'e}rence de qualit\IeC {\'e} et \IeC {\`a} un moyenneur GPU 5$\times $5 pris comme r\IeC {\'e}f\IeC {\'e}rence de rapidit\IeC {\'e}. Les param\IeC {\`e}tres du PI-LD/PI-PD sont $n=5$, $l=25$, $T_{max}=1$ et $T2_{max}=2$. La colonne 'Bruit\IeC {\'e}e' donne les mesures relatives \IeC {\`a} l'image d'entr\IeC {\'e}e corrompue par un bruit gaussien de moyenne nulle et d'\IeC {\'e}cart type $\sigma =25$. PI-LD s'ex\IeC {\'e}cute en 35~ms, PI-PD en 7,3~ms et BM3D en 4,3~s.}}{101}{table.7.2}
-\contentsline {table}{\numberline {7.3}{\ignorespaces Comparaison image par image de la qualit\IeC {\'e} de d\IeC {\'e}bruitage du filtre PI-PD RVB propos\IeC {\'e} par rapport \IeC {\`a} BM3D pris comme r\IeC {\'e}f\IeC {\'e}rence de qualit\IeC {\'e}. Les param\IeC {\`e}tres du PI-PD sont $n=4$, $l=48$, $T_{rvb-max}=5$. La colonne 'noisy' donne les mesures relatives \IeC {\`a} l'image d'entr\IeC {\'e}e corrompue par tirage de bruit gaussien sur chaque canal ( moyenne nulle, \IeC {\'e}cart type $\sigma =25$).}}{102}{table.7.3}
-\addvspace {10\p@ }
-\contentsline {table}{\numberline {8.1}{\ignorespaces Temps de transfert vers et depuis le GPU, en fonction de la dimension de l'image et de la profondeur des niveaux de gris. La colonne ``M\IeC {\'e}moire globale'' donne les temps mesur\IeC {\'e}s lorsque cette seule m\IeC {\'e}moire est employ\IeC {\'e}e.}}{104}{table.8.1}
-\contentsline {table}{\numberline {8.2}{\ignorespaces Pourcentage du temps d'ex\IeC {\'e}cution pris par les transferts de donn\IeC {\'e}es en fonction de la taille de fen\IeC {\^e}tre du filtre, pour les profondeurs 8 et 16 bits sur GPU C2070.}}{110}{table.8.2}
-\contentsline {table}{\numberline {8.3}{\ignorespaces Performances des filtres m\IeC {\'e}dians rapides en fonction des tailles d'image et de fen\IeC {\^e}tre du filtre, en variantes 8 et 16 bits de profondeursur GPU C2070.}}{111}{table.8.3}
-\contentsline {table}{\numberline {8.4}{\ignorespaces D\IeC {\'e}bits maximum effectifs $T_8$ and $T_{16}$ (en MP/s), respectivement pour les variantes 8 et 16 bits sur C2070.}}{111}{table.8.4}
-\addvspace {10\p@ }
-\contentsline {table}{\numberline {9.1}{\ignorespaces Performances des kernels effectuant la convolution non-s\IeC {\'e}parable sur le mod\IeC {\`e}le du listing \ref {lst-convo-gene3reg8}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} la seule ex\IeC {\'e}cution du kernel. Le d\IeC {\'e}bit global int\IeC {\`e}gre les temps de transfert. Les valeurs en gras correspondent au traitement de r\IeC {\'e}f\IeC {\'e}rence.}}{115}{table.9.1}
-\contentsline {table}{\numberline {9.2}{\ignorespaces Performances des kernels effectuant la convolution non-s\IeC {\'e}parable sur le mod\IeC {\`e}le du listing \ref {lst-convo-gene3reg8}, sur GPU GTX280. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} la seule ex\IeC {\'e}cution du kernel. Le d\IeC {\'e}bit global int\IeC {\`e}gre les temps de transfert. Les valeurs en gras correspondent au traitement de r\IeC {\'e}f\IeC {\'e}rence.}}{115}{table.9.2}
-\contentsline {table}{\numberline {9.3}{\ignorespaces Performances des kernels effectuant la convolution non-s\IeC {\'e}parable sur le mod\IeC {\`e}le du listing \ref {lst-convo-8x8pL3}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} la seule ex\IeC {\'e}cution du kernel. Le d\IeC {\'e}bit global int\IeC {\`e}gre les temps de transfert. Les valeurs en gras correspondent au traitement de r\IeC {\'e}f\IeC {\'e}rence. }}{117}{table.9.3}
-\contentsline {table}{\numberline {9.4}{\ignorespaces Performances des kernels effectuant la convolution s\IeC {\'e}parable sur le mod\IeC {\`e}le des listings \ref {lst-convo-1Dv} et \ref {lst-convo-1Dh}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} l'ex\IeC {\'e}cution des 2 kernels. Cette variante pr\IeC {\'e}sente des performances voisines de la solution Nvidia.}}{121}{table.9.4}
-\contentsline {table}{\numberline {9.5}{\ignorespaces Temps de transfert total depuis et vers le GPU, en fonction de la dimension de l'image. Extrait de la table \ref {tab-median-memcpy}.}}{123}{table.9.5}
-\contentsline {table}{\numberline {9.6}{\ignorespaces Dur\IeC {\'e}e de la copie depuis la m\IeC {\'e}moire globale vers la m\IeC {\'e}moire texture, en fonction de la taille de l'image.}}{123}{table.9.6}
-\contentsline {table}{\numberline {9.7}{\ignorespaces Performances des kernels effectuant la convolution s\IeC {\'e}parable optimis\IeC {\'e}e sur le mod\IeC {\`e}le des listings \ref {lst-convo-1Dv} et \ref {lst-convons-optim}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} l'ex\IeC {\'e}cution des 2 kernels.}}{124}{table.9.7}
-\contentsline {table}{\numberline {9.8}{\ignorespaces D\IeC {\'e}bit global en ms (incluant les transferts) des kernels effectuant la convolution s\IeC {\'e}parable sur le mod\IeC {\`e}le des listings \ref {lst-convo-1Dv} et \ref {lst-convons-optim}, sur GPU C2070.}}{124}{table.9.8}
+\contentsline {table}{\numberline {6.4}{\ignorespaces Comparaison des temps d'ex\IeC {\'e}cution de l'impl\IeC {\'e}mentation GPU (C2070) par rapport \IeC {\`a} l'impl\IeC {\'e}mentation CPU de r\IeC {\'e}f\IeC {\'e}rence, appliqu\IeC {\'e}s \IeC {\`a} une m\IeC {\^e}me image dilat\IeC {\'e}e (fig. \ref {fig-snakecpu-cochon512}) pour en adapter la taille.}}{75}{table.6.4}
+\addvspace {10\p@ }
+\contentsline {table}{\numberline {7.1}{\ignorespaces Temps de calcul et de transfert des impl\IeC {\'e}mentations compar\IeC {\'e}es. }}{98}{table.7.1}
+\contentsline {table}{\numberline {7.2}{\ignorespaces Comparaison image par image de la qualit\IeC {\'e} de d\IeC {\'e}bruitage des filtres PI-LD et PI-PD hybride propos\IeC {\'e} par rapport \IeC {\`a} BM3D pris comme r\IeC {\'e}f\IeC {\'e}rence de qualit\IeC {\'e} et \IeC {\`a} un moyenneur GPU 5$\times $5 pris comme r\IeC {\'e}f\IeC {\'e}rence de rapidit\IeC {\'e}. Les param\IeC {\`e}tres du PI-LD/PI-PD sont $n=5$, $l=25$, $T_{max}=1$ et $T2_{max}=2$. La colonne 'Bruit\IeC {\'e}e' donne les mesures relatives \IeC {\`a} l'image d'entr\IeC {\'e}e corrompue par un bruit gaussien de moyenne nulle et d'\IeC {\'e}cart type $\sigma =25$. PI-LD s'ex\IeC {\'e}cute en 35~ms, PI-PD en 7,3~ms et BM3D en 4,3~s.}}{99}{table.7.2}
+\contentsline {table}{\numberline {7.3}{\ignorespaces Comparaison image par image de la qualit\IeC {\'e} de d\IeC {\'e}bruitage du filtre PI-PD RVB propos\IeC {\'e} par rapport \IeC {\`a} BM3D pris comme r\IeC {\'e}f\IeC {\'e}rence de qualit\IeC {\'e}. Les param\IeC {\`e}tres du PI-PD sont $n=4$, $l=48$, $T_{rvb-max}=5$. La colonne 'noisy' donne les mesures relatives \IeC {\`a} l'image d'entr\IeC {\'e}e corrompue par tirage de bruit gaussien sur chaque canal ( moyenne nulle, \IeC {\'e}cart type $\sigma =25$).}}{100}{table.7.3}
+\addvspace {10\p@ }
+\contentsline {table}{\numberline {8.1}{\ignorespaces Temps de transfert vers et depuis le GPU, en fonction de la dimension de l'image et de la profondeur des niveaux de gris. La colonne ``M\IeC {\'e}moire globale'' donne les temps mesur\IeC {\'e}s lorsque cette seule m\IeC {\'e}moire est employ\IeC {\'e}e.}}{102}{table.8.1}
+\contentsline {table}{\numberline {8.2}{\ignorespaces Pourcentage du temps d'ex\IeC {\'e}cution pris par les transferts de donn\IeC {\'e}es en fonction de la taille de fen\IeC {\^e}tre du filtre, pour les profondeurs 8 et 16 bits sur GPU C2070.}}{108}{table.8.2}
+\contentsline {table}{\numberline {8.3}{\ignorespaces Performances des filtres m\IeC {\'e}dians rapides en fonction des tailles d'image et de fen\IeC {\^e}tre du filtre, en variantes 8 et 16 bits de profondeursur GPU C2070.}}{109}{table.8.3}
+\contentsline {table}{\numberline {8.4}{\ignorespaces D\IeC {\'e}bits maximum effectifs $T_8$ and $T_{16}$ (en MP/s), respectivement pour les variantes 8 et 16 bits sur C2070.}}{109}{table.8.4}
+\addvspace {10\p@ }
+\contentsline {table}{\numberline {9.1}{\ignorespaces Performances des kernels effectuant la convolution non-s\IeC {\'e}parable sur le mod\IeC {\`e}le du listing \ref {lst-convo-gene3reg8}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} la seule ex\IeC {\'e}cution du kernel. Le d\IeC {\'e}bit global int\IeC {\`e}gre les temps de transfert. Les valeurs en gras correspondent au traitement de r\IeC {\'e}f\IeC {\'e}rence.}}{113}{table.9.1}
+\contentsline {table}{\numberline {9.2}{\ignorespaces Performances des kernels effectuant la convolution non-s\IeC {\'e}parable sur le mod\IeC {\`e}le du listing \ref {lst-convo-gene3reg8}, sur GPU GTX280. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} la seule ex\IeC {\'e}cution du kernel. Le d\IeC {\'e}bit global int\IeC {\`e}gre les temps de transfert. Les valeurs en gras correspondent au traitement de r\IeC {\'e}f\IeC {\'e}rence.}}{113}{table.9.2}
+\contentsline {table}{\numberline {9.3}{\ignorespaces Performances des kernels effectuant la convolution non-s\IeC {\'e}parable sur le mod\IeC {\`e}le du listing \ref {lst-convo-8x8pL3}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} la seule ex\IeC {\'e}cution du kernel. Le d\IeC {\'e}bit global int\IeC {\`e}gre les temps de transfert. Les valeurs en gras correspondent au traitement de r\IeC {\'e}f\IeC {\'e}rence. }}{115}{table.9.3}
+\contentsline {table}{\numberline {9.4}{\ignorespaces Performances des kernels effectuant la convolution s\IeC {\'e}parable sur le mod\IeC {\`e}le des listings \ref {lst-convo-1Dv} et \ref {lst-convo-1Dh}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} l'ex\IeC {\'e}cution des 2 kernels. Cette variante pr\IeC {\'e}sente des performances voisines de la solution Nvidia.}}{119}{table.9.4}
+\contentsline {table}{\numberline {9.5}{\ignorespaces Temps de transfert total depuis et vers le GPU, en fonction de la dimension de l'image. Extrait de la table \ref {tab-median-memcpy}.}}{121}{table.9.5}
+\contentsline {table}{\numberline {9.6}{\ignorespaces Dur\IeC {\'e}e de la copie depuis la m\IeC {\'e}moire globale vers la m\IeC {\'e}moire texture, en fonction de la taille de l'image.}}{121}{table.9.6}
+\contentsline {table}{\numberline {9.7}{\ignorespaces Performances des kernels effectuant la convolution s\IeC {\'e}parable optimis\IeC {\'e}e sur le mod\IeC {\`e}le des listings \ref {lst-convo-1Dv} et \ref {lst-convons-optim}, sur GPU C2070. Le temps d'ex\IeC {\'e}cution correspond \IeC {\`a} l'ex\IeC {\'e}cution des 2 kernels.}}{122}{table.9.7}
+\contentsline {table}{\numberline {9.8}{\ignorespaces D\IeC {\'e}bit global en ms (incluant les transferts) des kernels effectuant la convolution s\IeC {\'e}parable sur le mod\IeC {\`e}le des listings \ref {lst-convo-1Dv} et \ref {lst-convons-optim}, sur GPU C2070.}}{122}{table.9.8}
+\addvspace {10\p@ }
 \addvspace {10\p@ }

diff --git a/THESE/these.out b/THESE/these.out
index 33e4ba2ee9b3ae86d1e89f8f11ec1055577838d5..b4254690647825daa381b3282e4c2d4b5e4ebe30 100644 (file)
--- a/THESE/these.out
+++ b/THESE/these.out
@@ -1,19 +1,21 @@
-\BOOKMARK [0][]{chapter.1}{1 Introduction}{}
-\BOOKMARK [0][]{chapter.2}{2 Les processeurs graphiques \(GPU\) NVidia\256}{}
+\BOOKMARK [-1][]{part.1}{I Introduction}{}
+\BOOKMARK [0][]{chapter.1}{1 Pr\351ambule}{part.1}
+\BOOKMARK [0][]{chapter.2}{2 Les processeurs graphiques \(GPU\) NVidia\256}{part.1}
 \BOOKMARK [1][]{section.2.1}{2.1 Pourquoi ?}{chapter.2}
 \BOOKMARK [1][]{section.2.2}{2.2 Comment ?}{chapter.2}
 \BOOKMARK [2][]{subsection.2.2.1}{2.2.1 Le mat\351riel}{section.2.2}
 \BOOKMARK [2][]{subsection.2.2.2}{2.2.2 Le logiciel}{section.2.2}
 \BOOKMARK [2][]{subsection.2.2.3}{2.2.3 L'occupancy}{section.2.2}
-\BOOKMARK [1][]{section.2.3}{2.3 Synth\350se des contraintes d'impl\351mentation}{chapter.2}
-\BOOKMARK [0][]{chapter.3}{3 Mod\350les d'image et de bruits - notations}{}
+\BOOKMARK [1][]{section.2.3}{2.3 Contraintes de conception}{chapter.2}
+\BOOKMARK [-1][]{part.2}{II Le traitement des images}{}
+\BOOKMARK [0][]{chapter.3}{3 Mod\350les d'image et de bruits - notations}{part.2}
 \BOOKMARK [1][]{section.3.1}{3.1 Mod\350le d'image bruit\351e}{chapter.3}
 \BOOKMARK [1][]{section.3.2}{3.2 Mod\350les de bruit}{chapter.3}
 \BOOKMARK [2][]{subsection.3.2.1}{3.2.1 Le bruit gaussien}{section.3.2}
 \BOOKMARK [2][]{subsection.3.2.2}{3.2.2 Le speckle}{section.3.2}
 \BOOKMARK [2][]{subsection.3.2.3}{3.2.3 Le bruit \253 sel et poivre \273}{section.3.2}
 \BOOKMARK [2][]{subsection.3.2.4}{3.2.4 Le bruit de Poisson}{section.3.2}
-\BOOKMARK [0][]{chapter.4}{4 Les techniques de r\351duction de bruit}{}
+\BOOKMARK [0][]{chapter.4}{4 Les techniques de r\351duction de bruit}{part.2}
 \BOOKMARK [1][]{section.4.1}{4.1 Les techniques de r\351duction de bruit}{chapter.4}
 \BOOKMARK [2][]{subsection.4.1.1}{4.1.1 Les op\351rateurs de base}{section.4.1}
 \BOOKMARK [3][]{subsubsection.4.1.1.1}{4.1.1.1 Le filtre de convolution}{subsection.4.1.1}
@@ -26,7 +28,7 @@
 \BOOKMARK [2][]{subsection.4.2.2}{4.2.2 Le filtre m\351dian}{section.4.2}
 \BOOKMARK [2][]{subsection.4.2.3}{4.2.3 Le filtre bilat\351ral}{section.4.2}
 \BOOKMARK [2][]{subsection.4.2.4}{4.2.4 Les filtres par patches}{section.4.2}
-\BOOKMARK [0][]{chapter.5}{5 Les techniques de segmentation des images}{}
+\BOOKMARK [0][]{chapter.5}{5 Les techniques de segmentation des images}{part.2}
 \BOOKMARK [1][]{section.5.1}{5.1 Introduction}{chapter.5}
 \BOOKMARK [1][]{section.5.2}{5.2 Les techniques de segmentation orient\351es r\351gions}{chapter.5}
 \BOOKMARK [2][]{subsection.5.2.1}{5.2.1 Analyse d'histogramme}{section.5.2}
@@ -41,7 +43,8 @@
 \BOOKMARK [2][]{subsection.5.4.4}{5.4.4 Level set et snakes}{section.5.4}
 \BOOKMARK [2][]{subsection.5.4.5}{5.4.5 Algorithmes hybrides}{section.5.4}
 \BOOKMARK [1][]{section.5.5}{5.5 Conclusion}{chapter.5}
-\BOOKMARK [0][]{chapter.6}{6 La segmentation par snake polygonal orient\351 r\351gions}{}
+\BOOKMARK [-1][]{part.3}{III Algorithmes GPU rapides pour la r\351duction de bruit et la segmentation}{}
+\BOOKMARK [0][]{chapter.6}{6 La segmentation par snake polygonal orient\351 r\351gions}{part.3}
 \BOOKMARK [1][]{section.6.1}{6.1 Introduction}{chapter.6}
 \BOOKMARK [1][]{section.6.2}{6.2 Pr\351sentation de l'algorithme}{chapter.6}
 \BOOKMARK [2][]{subsection.6.2.1}{6.2.1 Formulation}{section.6.2}
@@ -55,7 +58,7 @@
 \BOOKMARK [2][]{subsection.6.3.3}{6.3.3 Performances}{section.6.3}
 \BOOKMARK [2][]{subsection.6.3.4}{6.3.4 D\351termination intelligente du contour initial}{section.6.3}
 \BOOKMARK [2][]{subsection.6.3.5}{6.3.5 Conclusion}{section.6.3}
-\BOOKMARK [0][]{chapter.7}{7 R\351duction de bruit par recherche des lignes de niveaux}{}
+\BOOKMARK [0][]{chapter.7}{7 R\351duction de bruit par recherche des lignes de niveaux}{part.3}
 \BOOKMARK [1][]{section.7.1}{7.1 Introduction}{chapter.7}
 \BOOKMARK [1][]{section.7.2}{7.2 Pr\351sentation de l'algorithme}{chapter.7}
 \BOOKMARK [2][]{subsection.7.2.1}{7.2.1 Formulation}{section.7.2}
@@ -71,7 +74,7 @@
 \BOOKMARK [2][]{subsection.7.5.1}{7.5.1 Expression du crit\350re}{section.7.5}
 \BOOKMARK [2][]{subsection.7.5.2}{7.5.2 R\351sultats}{section.7.5}
 \BOOKMARK [1][]{section.7.6}{7.6 Conclusion}{chapter.7}
-\BOOKMARK [0][]{chapter.8}{8 Le filtre m\351dian sur GPU}{}
+\BOOKMARK [0][]{chapter.8}{8 Le filtre m\351dian sur GPU}{part.3}
 \BOOKMARK [1][]{section.8.1}{8.1 Introduction}{chapter.8}
 \BOOKMARK [1][]{section.8.2}{8.2 Les transferts de donn\351es}{chapter.8}
 \BOOKMARK [1][]{section.8.3}{8.3 Utilisation des registres}{chapter.8}
@@ -79,10 +82,12 @@
 \BOOKMARK [2][]{subsection.8.3.2}{8.3.2 Masquage des latences}{section.8.3}
 \BOOKMARK [1][]{section.8.4}{8.4 R\351sultats}{chapter.8}
 \BOOKMARK [1][]{section.8.5}{8.5 Conclusion}{chapter.8}
-\BOOKMARK [0][]{chapter.9}{9 Les filtres de convolution sur GPU}{}
+\BOOKMARK [0][]{chapter.9}{9 Les filtres de convolution sur GPU}{part.3}
 \BOOKMARK [1][]{section.9.1}{9.1 Introduction}{chapter.9}
 \BOOKMARK [1][]{section.9.2}{9.2 Impl\351mentation g\351n\351rique de la convolution non s\351parable sur GPU}{chapter.9}
 \BOOKMARK [1][]{section.9.3}{9.3 Impl\351mentation optimis\351e de la convolution non s\351parable sur GPU}{chapter.9}
 \BOOKMARK [1][]{section.9.4}{9.4 Cas de la convolution s\351parable}{chapter.9}
 \BOOKMARK [1][]{section.9.5}{9.5 Conclusion}{chapter.9}
-\BOOKMARK [0][]{chapter.10}{10 Conclusion g\351n\351rale}{}
+\BOOKMARK [-1][]{part.4}{IV Conclusions et perspectives}{}
+\BOOKMARK [0][]{chapter.10}{10 Conclusion g\351n\351rale}{part.4}
+\BOOKMARK [0][]{chapter.11}{11 Remerciements}{part.4}

diff --git a/THESE/these.pdf b/THESE/these.pdf
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diff --git a/THESE/these.tex b/THESE/these.tex
index 600f52a1db627611f08a9c6aec3b5accc4d50a78..866d39caa1cbfd80c25db5b51e1913eb55fd3465 100644 (file)
--- a/THESE/these.tex
+++ b/THESE/these.tex
@@ -53,7 +53,7 @@
 %% 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[]{Algorithmes rapides pour le traitement des images bruitées sur GPU}{17 novembre 2013}{XXX}
+\declarethesis[]{Algorithmes rapides pour le filtrage et la segmentation des images bruitées sur GPU}{17 novembre 2013}{XXX}
       
 %%--------------------  
 %% Set the author of the PhD thesis
@@ -127,20 +127,18 @@ Dans cette thèse, nous proposons des méthodes conduisant à des implémentatio
 \usepackage{bbold}
 \def\indentit{\mbox{l\hspace{-0.55em}1}}
 
-   
+  
 \begin{document}
-    
-\tableofcontents
-                                                                         
+\pagenumbering{arabic}  
+     
+\tableofcontents                                                                      
 %-------------------- 
-% The content of the PhD thesis
-% objectifs, cadre  
-% annonce du plan  
-\chapter{Introduction}          
+\part{Introduction}
+\chapter{Préambule}          
 \input{Chapters/chapter1/chapter1.tex}
 \chapter{Les processeurs graphiques (GPU) NVidia\textregistered}\label{ch-GPU}
 \input{Chapters/chapter1b/chapter1b.tex}
-\part*{Le traitement des images}
+\part{Le traitement des images}
 \section*{Introduction} 
 \input{Chapters/chapter2/chapter2.tex}
 \chapter{Modèles d'image et de bruits - notations}
@@ -150,7 +148,7 @@ Dans cette thèse, nous proposons des méthodes conduisant à des implémentatio
 \chapter{Les techniques de segmentation des images}
 \input{Chapters/chapter2/chapter2d.tex}
 
-\part*{Algorithmes GPU rapides pour la réduction de bruit et la segmentation}
+\part{Algorithmes GPU rapides pour la réduction de bruit et la segmentation}
 \chapter{La segmentation par snake polygonal orienté régions\label{ch-snake}}
 \input{Chapters/chapter3/chapter3.tex}     
 
@@ -162,16 +160,13 @@ Dans cette thèse, nous proposons des méthodes conduisant à des implémentatio
 
 \chapter{Les filtres de convolution sur GPU\label{ch-convo}}
 \input{Chapters/chapter6/chapter6.tex} 
-% présentation, rôle   
-% détails : convo NON SEP, convo SEP, discussion Shared mem
-% résultats, conclusion  
-% intégrer logiciels dans sections
+
+\part{Conclusions et perspectives}
 \chapter{Conclusion générale}
 \input{Chapters/chapter1/conclusion.tex}
 
-%\pagebreak
-%\section*{Remerciements}
-%\input{Chapters/chapter1/rem.tex}
+\chapter{Remerciements}
+\input{Chapters/chapter1/rem.tex}
 
 %--------------------
 % Bibliography, indexes...

diff --git a/THESE/these.toc b/THESE/these.toc
index a754887aa9714357884df6c9c11eef76999d47dd..cbb9c2f6603bde82a44bae5940b844e3f7293a59 100644 (file)
--- a/THESE/these.toc
+++ b/THESE/these.toc
@@ -1,89 +1,94 @@
 \select@language {french}
-\contentsline {chapter}{\numberline {1}Introduction}{9}{chapter.1}
-\contentsline {chapter}{\numberline {2}Les processeurs graphiques (GPU) NVidia\textregistered }{13}{chapter.2}
-\contentsline {section}{\numberline {2.1}Pourquoi ?}{13}{section.2.1}
-\contentsline {section}{\numberline {2.2}Comment ?}{15}{section.2.2}
-\contentsline {subsection}{\numberline {2.2.1}Le mat\IeC {\'e}riel}{15}{subsection.2.2.1}
-\contentsline {subsection}{\numberline {2.2.2}Le logiciel}{16}{subsection.2.2.2}
-\contentsline {subsection}{\numberline {2.2.3}L'occupancy}{17}{subsection.2.2.3}
-\contentsline {section}{\numberline {2.3}Synth\IeC {\`e}se des contraintes d'impl\IeC {\'e}mentation}{18}{section.2.3}
-\contentsline {chapter}{\numberline {3}Mod\IeC {\`e}les d'image et de bruits - notations}{25}{chapter.3}
-\contentsline {section}{\numberline {3.1}Mod\IeC {\`e}le d'image bruit\IeC {\'e}e}{25}{section.3.1}
-\contentsline {section}{\numberline {3.2}Mod\IeC {\`e}les de bruit}{25}{section.3.2}
-\contentsline {subsection}{\numberline {3.2.1}Le bruit gaussien}{25}{subsection.3.2.1}
-\contentsline {subsection}{\numberline {3.2.2}Le speckle}{26}{subsection.3.2.2}
-\contentsline {subsection}{\numberline {3.2.3}Le bruit \FB@og sel et poivre \FB@fg {}}{26}{subsection.3.2.3}
-\contentsline {subsection}{\numberline {3.2.4}Le bruit de Poisson}{27}{subsection.3.2.4}
-\contentsline {chapter}{\numberline {4}Les techniques de r\IeC {\'e}duction de bruit}{29}{chapter.4}
-\contentsline {section}{\numberline {4.1}Les techniques de r\IeC {\'e}duction de bruit}{29}{section.4.1}
-\contentsline {subsection}{\numberline {4.1.1}Les op\IeC {\'e}rateurs de base}{30}{subsection.4.1.1}
-\contentsline {subsubsection}{\numberline {4.1.1.1}Le filtre de convolution}{30}{subsubsection.4.1.1.1}
-\contentsline {subsubsection}{\numberline {4.1.1.2}Le filtre m\IeC {\'e}dian}{31}{subsubsection.4.1.1.2}
-\contentsline {subsubsection}{\numberline {4.1.1.3}Le filtre bilat\IeC {\'e}ral}{32}{subsubsection.4.1.1.3}
-\contentsline {subsubsection}{\numberline {4.1.1.4}Les algorithmes de filtrage par dictionnaire}{32}{subsubsection.4.1.1.4}
-\contentsline {subsection}{\numberline {4.1.2}Les algorithmes de filtrage par patches}{34}{subsection.4.1.2}
-\contentsline {section}{\numberline {4.2}Les impl\IeC {\'e}mentations sur GPU des algorithmes de filtrage}{35}{section.4.2}
-\contentsline {subsection}{\numberline {4.2.1}Le filtrage par convolution}{35}{subsection.4.2.1}
-\contentsline {subsection}{\numberline {4.2.2}Le filtre m\IeC {\'e}dian}{36}{subsection.4.2.2}
-\contentsline {subsection}{\numberline {4.2.3}Le filtre bilat\IeC {\'e}ral}{37}{subsection.4.2.3}
-\contentsline {subsection}{\numberline {4.2.4}Les filtres par patches}{39}{subsection.4.2.4}
-\contentsline {chapter}{\numberline {5}Les techniques de segmentation des images}{41}{chapter.5}
-\contentsline {section}{\numberline {5.1}Introduction}{41}{section.5.1}
-\contentsline {section}{\numberline {5.2}Les techniques de segmentation orient\IeC {\'e}es r\IeC {\'e}gions}{41}{section.5.2}
-\contentsline {subsection}{\numberline {5.2.1}Analyse d'histogramme}{42}{subsection.5.2.1}
-\contentsline {subsection}{\numberline {5.2.2}Partitionnement de graphe}{42}{subsection.5.2.2}
-\contentsline {subsection}{\numberline {5.2.3}kernel-means, mean-shift et apparent\IeC {\'e}s}{45}{subsection.5.2.3}
-\contentsline {section}{\numberline {5.3}Les techniques de segmentation par contours actifs, ou snakes}{47}{section.5.3}
-\contentsline {subsection}{\numberline {5.3.1}M\IeC {\'e}thodes hybrides}{49}{subsection.5.3.1}
-\contentsline {section}{\numberline {5.4}Les impl\IeC {\'e}mentations des techniques de segmentation sur GPU}{49}{section.5.4}
-\contentsline {subsection}{\numberline {5.4.1}Calcul d'histogramme}{50}{subsection.5.4.1}
-\contentsline {subsection}{\numberline {5.4.2}Partitionnement de graphe}{50}{subsection.5.4.2}
-\contentsline {subsection}{\numberline {5.4.3}K-means, mean-shift et apparent\IeC {\'e}s}{51}{subsection.5.4.3}
-\contentsline {subsection}{\numberline {5.4.4}Level set et snakes}{54}{subsection.5.4.4}
-\contentsline {subsection}{\numberline {5.4.5}Algorithmes hybrides}{56}{subsection.5.4.5}
-\contentsline {section}{\numberline {5.5}Conclusion}{57}{section.5.5}
-\contentsline {chapter}{\numberline {6}La segmentation par snake polygonal orient\IeC {\'e} r\IeC {\'e}gions}{61}{chapter.6}
-\contentsline {section}{\numberline {6.1}Introduction}{61}{section.6.1}
-\contentsline {section}{\numberline {6.2}Pr\IeC {\'e}sentation de l'algorithme}{61}{section.6.2}
-\contentsline {subsection}{\numberline {6.2.1}Formulation}{61}{subsection.6.2.1}
-\contentsline {subsection}{\numberline {6.2.2}Optimisation des calculs}{62}{subsection.6.2.2}
-\contentsline {subsection}{\numberline {6.2.3}Impl\IeC {\'e}mentation s\IeC {\'e}quentielle}{64}{subsection.6.2.3}
-\contentsline {subsection}{\numberline {6.2.4}Performances}{66}{subsection.6.2.4}
-\contentsline {section}{\numberline {6.3}Impl\IeC {\'e}mentation parall\IeC {\`e}le GPU du snake polygonal}{68}{section.6.3}
-\contentsline {subsection}{\numberline {6.3.1}Pr\IeC {\'e}-calculs des images cumul\IeC {\'e}es}{69}{subsection.6.3.1}
+\contentsline {part}{I\hspace {1em}Introduction}{5}{part.1}
+\contentsline {chapter}{\numberline {1}Pr\IeC {\'e}ambule}{7}{chapter.1}
+\contentsline {chapter}{\numberline {2}Les processeurs graphiques (GPU) NVidia\textregistered }{11}{chapter.2}
+\contentsline {section}{\numberline {2.1}Pourquoi ?}{11}{section.2.1}
+\contentsline {section}{\numberline {2.2}Comment ?}{13}{section.2.2}
+\contentsline {subsection}{\numberline {2.2.1}Le mat\IeC {\'e}riel}{13}{subsection.2.2.1}
+\contentsline {subsection}{\numberline {2.2.2}Le logiciel}{13}{subsection.2.2.2}
+\contentsline {subsection}{\numberline {2.2.3}L'occupancy}{14}{subsection.2.2.3}
+\contentsline {section}{\numberline {2.3}Contraintes de conception}{16}{section.2.3}
+\contentsline {part}{II\hspace {1em}Le traitement des images}{19}{part.2}
+\contentsline {chapter}{\numberline {3}Mod\IeC {\`e}les d'image et de bruits - notations}{23}{chapter.3}
+\contentsline {section}{\numberline {3.1}Mod\IeC {\`e}le d'image bruit\IeC {\'e}e}{23}{section.3.1}
+\contentsline {section}{\numberline {3.2}Mod\IeC {\`e}les de bruit}{23}{section.3.2}
+\contentsline {subsection}{\numberline {3.2.1}Le bruit gaussien}{23}{subsection.3.2.1}
+\contentsline {subsection}{\numberline {3.2.2}Le speckle}{24}{subsection.3.2.2}
+\contentsline {subsection}{\numberline {3.2.3}Le bruit \FB@og sel et poivre \FB@fg {}}{24}{subsection.3.2.3}
+\contentsline {subsection}{\numberline {3.2.4}Le bruit de Poisson}{25}{subsection.3.2.4}
+\contentsline {chapter}{\numberline {4}Les techniques de r\IeC {\'e}duction de bruit}{27}{chapter.4}
+\contentsline {section}{\numberline {4.1}Les techniques de r\IeC {\'e}duction de bruit}{27}{section.4.1}
+\contentsline {subsection}{\numberline {4.1.1}Les op\IeC {\'e}rateurs de base}{28}{subsection.4.1.1}
+\contentsline {subsubsection}{\numberline {4.1.1.1}Le filtre de convolution}{28}{subsubsection.4.1.1.1}
+\contentsline {subsubsection}{\numberline {4.1.1.2}Le filtre m\IeC {\'e}dian}{29}{subsubsection.4.1.1.2}
+\contentsline {subsubsection}{\numberline {4.1.1.3}Le filtre bilat\IeC {\'e}ral}{30}{subsubsection.4.1.1.3}
+\contentsline {subsubsection}{\numberline {4.1.1.4}Les algorithmes de filtrage par dictionnaire}{30}{subsubsection.4.1.1.4}
+\contentsline {subsection}{\numberline {4.1.2}Les algorithmes de filtrage par patches}{32}{subsection.4.1.2}
+\contentsline {section}{\numberline {4.2}Les impl\IeC {\'e}mentations sur GPU des algorithmes de filtrage}{33}{section.4.2}
+\contentsline {subsection}{\numberline {4.2.1}Le filtrage par convolution}{33}{subsection.4.2.1}
+\contentsline {subsection}{\numberline {4.2.2}Le filtre m\IeC {\'e}dian}{34}{subsection.4.2.2}
+\contentsline {subsection}{\numberline {4.2.3}Le filtre bilat\IeC {\'e}ral}{35}{subsection.4.2.3}
+\contentsline {subsection}{\numberline {4.2.4}Les filtres par patches}{37}{subsection.4.2.4}
+\contentsline {chapter}{\numberline {5}Les techniques de segmentation des images}{39}{chapter.5}
+\contentsline {section}{\numberline {5.1}Introduction}{39}{section.5.1}
+\contentsline {section}{\numberline {5.2}Les techniques de segmentation orient\IeC {\'e}es r\IeC {\'e}gions}{39}{section.5.2}
+\contentsline {subsection}{\numberline {5.2.1}Analyse d'histogramme}{40}{subsection.5.2.1}
+\contentsline {subsection}{\numberline {5.2.2}Partitionnement de graphe}{40}{subsection.5.2.2}
+\contentsline {subsection}{\numberline {5.2.3}kernel-means, mean-shift et apparent\IeC {\'e}s}{43}{subsection.5.2.3}
+\contentsline {section}{\numberline {5.3}Les techniques de segmentation par contours actifs, ou snakes}{45}{section.5.3}
+\contentsline {subsection}{\numberline {5.3.1}M\IeC {\'e}thodes hybrides}{47}{subsection.5.3.1}
+\contentsline {section}{\numberline {5.4}Les impl\IeC {\'e}mentations des techniques de segmentation sur GPU}{47}{section.5.4}
+\contentsline {subsection}{\numberline {5.4.1}Calcul d'histogramme}{48}{subsection.5.4.1}
+\contentsline {subsection}{\numberline {5.4.2}Partitionnement de graphe}{48}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.3}K-means, mean-shift et apparent\IeC {\'e}s}{49}{subsection.5.4.3}
+\contentsline {subsection}{\numberline {5.4.4}Level set et snakes}{52}{subsection.5.4.4}
+\contentsline {subsection}{\numberline {5.4.5}Algorithmes hybrides}{54}{subsection.5.4.5}
+\contentsline {section}{\numberline {5.5}Conclusion}{55}{section.5.5}
+\contentsline {part}{III\hspace {1em}Algorithmes GPU rapides pour la r\IeC {\'e}duction de bruit et la segmentation}{57}{part.3}
+\contentsline {chapter}{\numberline {6}La segmentation par snake polygonal orient\IeC {\'e} r\IeC {\'e}gions}{59}{chapter.6}
+\contentsline {section}{\numberline {6.1}Introduction}{59}{section.6.1}
+\contentsline {section}{\numberline {6.2}Pr\IeC {\'e}sentation de l'algorithme}{59}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}Formulation}{59}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}Optimisation des calculs}{60}{subsection.6.2.2}
+\contentsline {subsection}{\numberline {6.2.3}Impl\IeC {\'e}mentation s\IeC {\'e}quentielle}{63}{subsection.6.2.3}
+\contentsline {subsection}{\numberline {6.2.4}Performances}{64}{subsection.6.2.4}
+\contentsline {section}{\numberline {6.3}Impl\IeC {\'e}mentation parall\IeC {\`e}le GPU du snake polygonal}{67}{section.6.3}
+\contentsline {subsection}{\numberline {6.3.1}Pr\IeC {\'e}-calculs des images cumul\IeC {\'e}es}{68}{subsection.6.3.1}
 \contentsline {subsection}{\numberline {6.3.2}Calcul des contributions des segments}{70}{subsection.6.3.2}
-\contentsline {subsubsection}{\numberline {6.3.2.1}Cas particulier des segments dont la pente $k$ v\IeC {\'e}rifie $|k|\leq 1$}{75}{subsubsection.6.3.2.1}
-\contentsline {subsection}{\numberline {6.3.3}Performances}{76}{subsection.6.3.3}
-\contentsline {subsection}{\numberline {6.3.4}D\IeC {\'e}termination intelligente du contour initial}{76}{subsection.6.3.4}
-\contentsline {subsection}{\numberline {6.3.5}Conclusion}{78}{subsection.6.3.5}
-\contentsline {chapter}{\numberline {7}R\IeC {\'e}duction de bruit par recherche des lignes de niveaux}{81}{chapter.7}
-\contentsline {section}{\numberline {7.1}Introduction}{81}{section.7.1}
-\contentsline {section}{\numberline {7.2}Pr\IeC {\'e}sentation de l'algorithme}{82}{section.7.2}
-\contentsline {subsection}{\numberline {7.2.1}Formulation}{82}{subsection.7.2.1}
-\contentsline {subsubsection}{\numberline {7.2.1.1}Isolines \IeC {\`a} un seul segment}{82}{subsubsection.7.2.1.1}
-\contentsline {subsubsection}{\numberline {7.2.1.2}Isolines compos\IeC {\'e}es de plusieurs segments - crit\IeC {\`e}re d'allongement}{84}{subsubsection.7.2.1.2}
-\contentsline {section}{\numberline {7.3}Mod\IeC {\'e}lisation des isolines pour l'impl\IeC {\'e}mentation parall\IeC {\`e}le sur GPU}{85}{section.7.3}
-\contentsline {subsection}{\numberline {7.3.1}Isolines \IeC {\'e}valu\IeC {\'e}es semi-globalement}{86}{subsection.7.3.1}
-\contentsline {subsection}{\numberline {7.3.2}Isolines \IeC {\`a} segments pr\IeC {\'e}-\IeC {\'e}valu\IeC {\'e}s - mod\IeC {\`e}le PI-PD}{88}{subsection.7.3.2}
-\contentsline {subsection}{\numberline {7.3.3}Mod\IeC {\`e}le PI-PD hybride}{90}{subsection.7.3.3}
-\contentsline {subsubsection}{\numberline {7.3.3.1}Le d\IeC {\'e}tecteur de zone \IeC {\`a} faible pente}{91}{subsubsection.7.3.3.1}
-\contentsline {section}{\numberline {7.4}R\IeC {\'e}sultats}{94}{section.7.4}
-\contentsline {section}{\numberline {7.5}Extension aux images couleurs}{96}{section.7.5}
-\contentsline {subsection}{\numberline {7.5.1}Expression du crit\IeC {\`e}re}{96}{subsection.7.5.1}
-\contentsline {subsection}{\numberline {7.5.2}R\IeC {\'e}sultats}{97}{subsection.7.5.2}
-\contentsline {section}{\numberline {7.6}Conclusion}{99}{section.7.6}
-\contentsline {chapter}{\numberline {8}Le filtre m\IeC {\'e}dian sur GPU}{103}{chapter.8}
-\contentsline {section}{\numberline {8.1}Introduction}{103}{section.8.1}
-\contentsline {section}{\numberline {8.2}Les transferts de donn\IeC {\'e}es}{103}{section.8.2}
-\contentsline {section}{\numberline {8.3}Utilisation des registres}{104}{section.8.3}
-\contentsline {subsection}{\numberline {8.3.1}La s\IeC {\'e}lection de la valeur m\IeC {\'e}diane}{105}{subsection.8.3.1}
-\contentsline {subsection}{\numberline {8.3.2}Masquage des latences}{107}{subsection.8.3.2}
-\contentsline {section}{\numberline {8.4}R\IeC {\'e}sultats}{109}{section.8.4}
-\contentsline {section}{\numberline {8.5}Conclusion}{111}{section.8.5}
-\contentsline {chapter}{\numberline {9}Les filtres de convolution sur GPU}{113}{chapter.9}
-\contentsline {section}{\numberline {9.1}Introduction}{113}{section.9.1}
-\contentsline {section}{\numberline {9.2}Impl\IeC {\'e}mentation g\IeC {\'e}n\IeC {\'e}rique de la convolution non s\IeC {\'e}parable sur GPU}{113}{section.9.2}
-\contentsline {section}{\numberline {9.3}Impl\IeC {\'e}mentation optimis\IeC {\'e}e de la convolution non s\IeC {\'e}parable sur GPU}{115}{section.9.3}
-\contentsline {section}{\numberline {9.4}Cas de la convolution s\IeC {\'e}parable}{119}{section.9.4}
-\contentsline {section}{\numberline {9.5}Conclusion}{124}{section.9.5}
+\contentsline {subsubsection}{\numberline {6.3.2.1}Cas particulier des segments dont la pente $k$ v\IeC {\'e}rifie $|k|\leq 1$}{73}{subsubsection.6.3.2.1}
+\contentsline {subsection}{\numberline {6.3.3}Performances}{74}{subsection.6.3.3}
+\contentsline {subsection}{\numberline {6.3.4}D\IeC {\'e}termination intelligente du contour initial}{75}{subsection.6.3.4}
+\contentsline {subsection}{\numberline {6.3.5}Conclusion}{76}{subsection.6.3.5}
+\contentsline {chapter}{\numberline {7}R\IeC {\'e}duction de bruit par recherche des lignes de niveaux}{79}{chapter.7}
+\contentsline {section}{\numberline {7.1}Introduction}{79}{section.7.1}
+\contentsline {section}{\numberline {7.2}Pr\IeC {\'e}sentation de l'algorithme}{80}{section.7.2}
+\contentsline {subsection}{\numberline {7.2.1}Formulation}{80}{subsection.7.2.1}
+\contentsline {subsubsection}{\numberline {7.2.1.1}Isolines \IeC {\`a} un seul segment}{80}{subsubsection.7.2.1.1}
+\contentsline {subsubsection}{\numberline {7.2.1.2}Isolines compos\IeC {\'e}es de plusieurs segments - crit\IeC {\`e}re d'allongement}{82}{subsubsection.7.2.1.2}
+\contentsline {section}{\numberline {7.3}Mod\IeC {\'e}lisation des isolines pour l'impl\IeC {\'e}mentation parall\IeC {\`e}le sur GPU}{83}{section.7.3}
+\contentsline {subsection}{\numberline {7.3.1}Isolines \IeC {\'e}valu\IeC {\'e}es semi-globalement}{84}{subsection.7.3.1}
+\contentsline {subsection}{\numberline {7.3.2}Isolines \IeC {\`a} segments pr\IeC {\'e}-\IeC {\'e}valu\IeC {\'e}s - mod\IeC {\`e}le PI-PD}{86}{subsection.7.3.2}
+\contentsline {subsection}{\numberline {7.3.3}Mod\IeC {\`e}le PI-PD hybride}{88}{subsection.7.3.3}
+\contentsline {subsubsection}{\numberline {7.3.3.1}Le d\IeC {\'e}tecteur de zone \IeC {\`a} faible pente}{89}{subsubsection.7.3.3.1}
+\contentsline {section}{\numberline {7.4}R\IeC {\'e}sultats}{92}{section.7.4}
+\contentsline {section}{\numberline {7.5}Extension aux images couleurs}{94}{section.7.5}
+\contentsline {subsection}{\numberline {7.5.1}Expression du crit\IeC {\`e}re}{94}{subsection.7.5.1}
+\contentsline {subsection}{\numberline {7.5.2}R\IeC {\'e}sultats}{95}{subsection.7.5.2}
+\contentsline {section}{\numberline {7.6}Conclusion}{97}{section.7.6}
+\contentsline {chapter}{\numberline {8}Le filtre m\IeC {\'e}dian sur GPU}{101}{chapter.8}
+\contentsline {section}{\numberline {8.1}Introduction}{101}{section.8.1}
+\contentsline {section}{\numberline {8.2}Les transferts de donn\IeC {\'e}es}{101}{section.8.2}
+\contentsline {section}{\numberline {8.3}Utilisation des registres}{103}{section.8.3}
+\contentsline {subsection}{\numberline {8.3.1}La s\IeC {\'e}lection de la valeur m\IeC {\'e}diane}{103}{subsection.8.3.1}
+\contentsline {subsection}{\numberline {8.3.2}Masquage des latences}{104}{subsection.8.3.2}
+\contentsline {section}{\numberline {8.4}R\IeC {\'e}sultats}{108}{section.8.4}
+\contentsline {section}{\numberline {8.5}Conclusion}{110}{section.8.5}
+\contentsline {chapter}{\numberline {9}Les filtres de convolution sur GPU}{111}{chapter.9}
+\contentsline {section}{\numberline {9.1}Introduction}{111}{section.9.1}
+\contentsline {section}{\numberline {9.2}Impl\IeC {\'e}mentation g\IeC {\'e}n\IeC {\'e}rique de la convolution non s\IeC {\'e}parable sur GPU}{111}{section.9.2}
+\contentsline {section}{\numberline {9.3}Impl\IeC {\'e}mentation optimis\IeC {\'e}e de la convolution non s\IeC {\'e}parable sur GPU}{113}{section.9.3}
+\contentsline {section}{\numberline {9.4}Cas de la convolution s\IeC {\'e}parable}{117}{section.9.4}
+\contentsline {section}{\numberline {9.5}Conclusion}{122}{section.9.5}
+\contentsline {part}{IV\hspace {1em}Conclusions et perspectives}{125}{part.4}
 \contentsline {chapter}{\numberline {10}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{127}{chapter.10}
+\contentsline {chapter}{\numberline {11}Remerciements}{129}{chapter.11}