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diff --git a/DOCS/snake_gvf_gpu_opencl.bib b/DOCS/snake_gvf_gpu_opencl.bib
new file mode 100644 (file)
index 0000000..d6a2773
--- /dev/null
+++ b/DOCS/snake_gvf_gpu_opencl.bib
@@ -0,0 +1,13 @@
+@article{
+year={2012},
+issn={1861-8200},
+journal={Journal of Real-Time Image Processing},
+doi={10.1007/s11554-012-0257-6},
+title={Real-time gradient vector flow on GPUs using OpenCL},
+url={http://dx.doi.org/10.1007/s11554-012-0257-6},
+publisher={Springer-Verlag},
+keywords={Gradient Vector Flow; GPU; OpenCL},
+author={Smistad, Erik and Elster, AnneC. and Lindseth, Frank},
+pages={1-8},
+language={English}
+}

diff --git a/THESE/Chapters/chapter2/chapter2.tex b/THESE/Chapters/chapter2/chapter2.tex
index b02fcc477106b63d58ef80b35a1f83645d39e7c0..9d12c732fc487b1dea6ce71955859b6f5f76be77 100644 (file)
--- a/THESE/Chapters/chapter2/chapter2.tex
+++ b/THESE/Chapters/chapter2/chapter2.tex
@@ -508,19 +508,46 @@ Récemment, Xiao et Liu ont décrit dans \cite{xiao2010efficient} une implément
 Dès 2003, on recense d'importants travaux liés à l'imagerie médicale mettant en \oe uvre des algorithmes \textit{level set} sur GPU. C'est le cas de \cite{lefohn2003inter,lefohn2003interactive} où les auteurs décrivent une solution de visualisation des coupes d'une mesure volumique réalisés par résonnance magnétique (IRM) en exploitant pour la première fois le caractère creux du système d'équations à résoudre, \textit{i.e.} variante narrow-band, contrairement à la première solution 2D présentée dans \cite{rumpf2001level} qui implémente la version standard. En ne transférant au GPU, pour chaque itération, que les petits pavés de données actifs et en les  rangeant alors de manière contigue en texture pour optimiser les accès en lecture, les auteurs sont ainsi parvenu à effectuer, pour des données volumiques de 256$\times$256$\times$175, entre 3.5 et 70 itérations par seconde, à comparer aux 50 itérations par seconde en 2D sur image de 128$^2$ pixels otenues dans \cite{rumpf2001level}. La limitation principale de cettesolution est celle des dimensions maximales admises pour une texture qui était de 2048$^2$ pour le GPU ATI Radeon 9800 pro employé (et programmé en openGL, car ni openCL ni CUDA n'étaient encore disponible à l'époque).
 Les autres solutions GPU proposées depuis sont également basées sur la variante \textit{narrow-band} (bande étroite) des \textit{level-set} \cite{lefohn2005streaming,cates2004gist,jeong2009scalable}, mais seule \cite{jeong2009scalable} s'affranchit des transferts CPU/GPU à chaque itération pour déterminer et transférer les pavés actifs. La solution retenue est d'employer les opérations atomiques pour assurer l'accès exclusif à la liste des pavés en mémoire GPU. Cela permet de descendre à 3~ms par itération pour une image de 512$^2$ pixels.
 
 Dès 2003, on recense d'importants travaux liés à l'imagerie médicale mettant en \oe uvre des algorithmes \textit{level set} sur GPU. C'est le cas de \cite{lefohn2003inter,lefohn2003interactive} où les auteurs décrivent une solution de visualisation des coupes d'une mesure volumique réalisés par résonnance magnétique (IRM) en exploitant pour la première fois le caractère creux du système d'équations à résoudre, \textit{i.e.} variante narrow-band, contrairement à la première solution 2D présentée dans \cite{rumpf2001level} qui implémente la version standard. En ne transférant au GPU, pour chaque itération, que les petits pavés de données actifs et en les  rangeant alors de manière contigue en texture pour optimiser les accès en lecture, les auteurs sont ainsi parvenu à effectuer, pour des données volumiques de 256$\times$256$\times$175, entre 3.5 et 70 itérations par seconde, à comparer aux 50 itérations par seconde en 2D sur image de 128$^2$ pixels otenues dans \cite{rumpf2001level}. La limitation principale de cettesolution est celle des dimensions maximales admises pour une texture qui était de 2048$^2$ pour le GPU ATI Radeon 9800 pro employé (et programmé en openGL, car ni openCL ni CUDA n'étaient encore disponible à l'époque).
 Les autres solutions GPU proposées depuis sont également basées sur la variante \textit{narrow-band} (bande étroite) des \textit{level-set} \cite{lefohn2005streaming,cates2004gist,jeong2009scalable}, mais seule \cite{jeong2009scalable} s'affranchit des transferts CPU/GPU à chaque itération pour déterminer et transférer les pavés actifs. La solution retenue est d'employer les opérations atomiques pour assurer l'accès exclusif à la liste des pavés en mémoire GPU. Cela permet de descendre à 3~ms par itération pour une image de 512$^2$ pixels.
 

-La plus performante des implémentations à ce jour est celle décrite dans \cite{Roberts:2010:WGA:1921479.1921499} qui parvient à des itérations dont la durée varie, sur GTX280,  de 1.8 à 6.5~ms pour des données volumiques de 256$^3$ pixels issues d'examen IRM, pour une moyenne de 3.2~ms sur les 2200 itérations de l'exemple fourni (cerveau en 7~s, Figure \ref{fig-l7-brain}). Une optimisation poussée y a été effectuée pour rendre l'algorithme efficace, en particulier au travers de la refonte du code responsable de la détermination des pavés actifs. Il parvient cette fois à déterminer l'ensemble minimal de pavés actifs et à rendre cette détermination efficace sur le GPU en gérant parallèlement plusieurs tampons, chacun associé à une direction particulière en 6-connexité. Une étape de résolution des doublons est ensuite effectuée avant de les compacter de manière contigue comme cele était déjà fait dans \cite{lefohn2003inter}. Toutefois, tenir à jour cette liste de pavés représente encore 77\% du temps de calcul après cette optimisation.
-%TODO dire qu'il n'utilise pas de shmem !
+La plus performante des implémentations à ce jour est celle décrite dans \cite{Roberts:2010:WGA:1921479.1921499} qui parvient à des itérations dont la durée varie, sur GTX280,  de 1.8 à 6.5~ms pour des données volumiques de 256$^3$ pixels issues d'examen IRM, pour une moyenne de 3.2~ms sur les 2200 itérations de l'exemple fourni (cerveau en 7~s, Figure \ref{fig-l7-brain}). Une optimisation poussée y a été effectuée pour rendre l'algorithme efficace, en particulier au travers de la refonte du code responsable de la détermination des pavés actifs. Il parvient cette fois à déterminer l'ensemble minimal de pavés actifs et à rendre cette détermination efficace sur le GPU en gérant parallèlement plusieurs tampons, chacun associé à une direction particulière en 6-connexité. Une étape de résolution des doublons est ensuite effectuée avant de les compacter de manière contigue comme cela était déjà fait dans \cite{lefohn2003inter}.Tout cela est réalisé sans recourir à la mémoire partagée qui s'avère complexe voire impossile à utiliser efficacement lorsque les éléments à accéder sont très irrégulièrement répartis en mémoire. 
+

 Ce faisant, le nombre cumulé total de pavés ainsi traités lors des 2200 itérations de la segmentation der l'image d'exemple s'élève à 294 millions à comparer aux 4877 millions traités par l'algorithme \textit{narrow-band} standard. Il est à noter que la durée d'exécution d'une itération dans cette variante dépend plus fortement de la proportion de pavés actifs que pour \textit{narrow-band} standard. Les deux courbes sont globalement affines et se croisent pour une proportion de pavés actifs proche de 10\%.
 Ce faisant, le nombre cumulé total de pavés ainsi traités lors des 2200 itérations de la segmentation der l'image d'exemple s'élève à 294 millions à comparer aux 4877 millions traités par l'algorithme \textit{narrow-band} standard. Il est à noter que la durée d'exécution d'une itération dans cette variante dépend plus fortement de la proportion de pavés actifs que pour \textit{narrow-band} standard. Les deux courbes sont globalement affines et se croisent pour une proportion de pavés actifs proche de 10\%.

-Cela peut représenter une piste pour une optimisation supplémentaire qui ne semble pas su justifier avec l'image et l'initialisation dont les performances sont détaillées, mais qui pourrait l'être dans d'autres conditions, comme peut le suggérer le temps de segmentation de 16~s nécessaire pour l'image des reins (Figure \ref{fig-l7-reins}) et de l'aorte, malgré des dimensions comparables.
+Si l'on considère que malgré les stratégies adoptées, tenir à jour cette liste de pavés représente encore 77\% du temps de calcul, cela peut représenter une piste pour une optimisation supplémentaire qui ne semble pas su justifier avec l'image et l'initialisation dont les performances sont détaillées, mais qui pourrait l'être dans d'autres conditions, comme peut le suggérer le temps de segmentation de 16~s nécessaire pour l'image des reins (Figure \ref{fig-l7-reins}) et de l'aorte, aux dimensions comparables.

 
 \begin{figure}
   \centering
 \subfigure[Cerveau 256$\times$256$\times$256 en 7~s]{\label{fig-l7-brain}\includegraphics[height=4cm]{/home/zulu/Documents/these_gilles/THESE/Chapters/chapter2/img/l7-brain7s.png}}\quad
 \subfigure[Reins et aorte, 256$\times$256$\times$272 en 16~s]{\label{fig-l7-reins}\includegraphics[height=4cm]{/home/zulu/Documents/these_gilles/THESE/Chapters/chapter2/img/l7-reins16s.png}}
 \caption{Segmentation d'images issues d'examens IRM par la méthode des level set à bande étroite.}
 
 \begin{figure}
   \centering
 \subfigure[Cerveau 256$\times$256$\times$256 en 7~s]{\label{fig-l7-brain}\includegraphics[height=4cm]{/home/zulu/Documents/these_gilles/THESE/Chapters/chapter2/img/l7-brain7s.png}}\quad
 \subfigure[Reins et aorte, 256$\times$256$\times$272 en 16~s]{\label{fig-l7-reins}\includegraphics[height=4cm]{/home/zulu/Documents/these_gilles/THESE/Chapters/chapter2/img/l7-reins16s.png}}
 \caption{Segmentation d'images issues d'examens IRM par la méthode des level set à bande étroite.}

-\label{fig-meanshift-castle}
+\label{fig-l7-narrow}

 \end{figure}
 
 \end{figure}
 

+Les algorithmes de type \textit{snake}, très coûteux en temps de calcul, pouvaient prétendre à bénéficier largement de la technologie des GPU pour améliorer leurs performances, mais seule la variante paramétrique GVF à véritablement été implémentée de manière spécifique et efficace \cite{snakegvf06, bauer2009segmentation, li2011robust, snakegvfopencl12}. Les variantes de type géométrique, principalement en raison de l'irrégularité des motifs d'accès à la mémoire, restent à ce jour sans implémentation GPU.
+Parmi les premières solutions décrites, \cite{snakegvf06} propose une implémentation réalisée en openGL, où les données de gradient sont compactées en texture RVBA de manière à s'affranchir du format 16 bits de la représentation : les deux premiers canaux R et V contiennent les valeursreprésentant respectivement le gradients selon $dx$ et $dy$ sous une forme codée par la valeurs des 2 autres canaux. 
+Par ailleurs, une approximation du système linéaire à résoudre est proposée afin de donner une structure bande symétrique à la matrice à inverser, ce qui améliore considérablement l'efficacité des accès aux données au travers du cache.
+
+Les performances annoncées montrent tout d'abord que l'approximation adoptée n'a qu'un impact extrêmement limité sur le résulat de la segmentation avec un écart radial maximal inférieur à 1.3 pixel par rapport au calcul exact effectué sur CPU. Enfin, la segmentation de l'image d'exemple en 1024$^2$ pixels s'effectue en un total de 11~s après l'initialisation manuelle reproduite à la figure \ref{fig-snakegvf}. Cela est annoncé comme presque 30 fois plus rapide que l'implémentation CPU de référence, mais demeure beaucoup trop lent pour un usage interactif.
+
+Une solution directe employant la transformée de fourier pour inverser le système à résoudre a été décrite récemment dans  \cite{zheng2012fast}et programmée en employant la bibliothèque openGL. Les exemples fournis montrent des objets segmentés dans des images d'environ 10000 pixels en une durée de l'ordre de la demi seconde.   
+
+En adaptant sur GPU une variante dite FD-snake \cite{li2011robust} du snake GVF (pour Fourier Descriptors) permettant une convergence plus rapide et un calcul parallèle beaucoup plus adapté au GPU, Li \textit{et al.} parviennent quant à eux à suivre les déformations d'un contour en temps réel dans des images issues d'examens échographique ; Un contour de 100 points pouvant converger convenablement en à peine 30~ms. Une contribution supplémentaire de cette implémentation est de permettre une initialisation simplifiée et semi-automatique du contour. 
+
+La plus aboutie des implémentations actuelles du snake GVF est enfin celle présentée par Smistad \textit{et al.} dans \cite{snakegvfopencl12} et où les auteurs ont concentré leur effort sur l'optimisation des accès mémoire lors du calcul du GVF. Ils ont comparé 8 combinaisons possibles impliquant l'emploi des mémoires partagée et de texture ainsi que la représentation des nombres selon le format classique 32 bits ou selon un format compressé sur 16 bits. Il en ressort que l'association la plus performante est celle des textures et du format de données sur 16 bits.
+Les performances sont alors nettement en hausse avec des segmentations d'images médicales d'IRM de 512$^2$ pixels effectuées en 41~ms sur Nvidia C2070 et 28~ms sur ATI 5870 (512 itérations). L'implémentation réalisée en openGL permet d'exécuter le code sur les GPU des deux principaux fabricants.   
+
+\subsection{Algorithmes hybrides}
+Le détecteur de contour \textit{gPb} décrit dans \cite{arbelaez2011contour} et que l'on considère comme la référence actuelle pour la semgentation d'objets et personnages dans des image naturelles, à été implémenté en CUDA par Catanzaro \textit{et al.} et est décrit dans \cite{5459410}. La qualité des contours extraits y est préservée et le temps de traitement y est réduit d'un facteur supérieur à 100 : les contours des images de 0.15~MP de la base de test BSDS \cite{martin2001database} sont ainsi traitées en 2 secondes environ sur GPU C1060.
+L'apport principal de ces travaux réside dans la solution conçue pour le calcul des histogrammes locaux, qui dans l'algorithme original s'étendaient sur des demi-disques centrés sur chaque pixel. La parallélisation réalisée fait l'approximation de chaque demi-disque en un rectangle de même surface dont un des grands cotés à le centre du disque pour milieu. Les rectangles sont ensuite pivotés par une rotation basée sur la discrétisation de Bresenham \cite{bresenham1965algorithm} pour en aligner les cotés avec les cotés de l'image et pouvoir employer la technique des images cumulées pour calculer rapidement l'histogramme.   
+La figure \ref{fig-gPb} présente quelques résultats d'extraction de contours.
+\begin{figure}
+  \centering
+\includegraphics[height=4cm]{/home/zulu/Documents/these_gilles/THESE/Chapters/chapter2/img/gPb_examples.png}
+\caption{Extraction de contour par la version GPU de l'algorithme gPb. Les images sont issues de la base BSDS  \cite{martin2001database}}
+\label{fig-gPb}
+\end{figure}
+
+     
+
+

 
 
 
 
 
 

diff --git a/THESE/Chapters/chapter2/img/gPb_examples.png b/THESE/Chapters/chapter2/img/gPb_examples.png
new file mode 100644 (file)
index 0000000..f6084e1
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diff --git a/THESE/biblio.bib b/THESE/biblio.bib
index 850c5b55c13b81250a6127276d1b2abf470ad644..92f642f2b1cc1113d3fb01a94a0ec6d12242d009 100755 (executable)
--- a/THESE/biblio.bib
+++ b/THESE/biblio.bib
@@ -1226,7 +1226,7 @@ pages={166-175}
   year={2011},
   organization={IEEE}
 }
   year={2011},
   organization={IEEE}
 }

-@incollection{
+@incollection{snakegvf06,

 year={2006},
 isbn={978-3-540-48628-2},
 booktitle={Advances in Visual Computing},
 year={2006},
 isbn={978-3-540-48628-2},
 booktitle={Advances in Visual Computing},


@@ -1240,3 +1240,59 @@ publisher={Springer Berlin Heidelberg},
 author={He, Zhiyu and Kuester, Falko},
 pages={191-201}
 }
 author={He, Zhiyu and Kuester, Falko},
 pages={191-201}
 }

+@article{snakegvfopencl12,
+year={2012},
+issn={1861-8200},
+journal={Journal of Real-Time Image Processing},
+doi={10.1007/s11554-012-0257-6},
+title={Real-time gradient vector flow on GPUs using OpenCL},
+url={http://dx.doi.org/10.1007/s11554-012-0257-6},
+publisher={Springer-Verlag},
+keywords={Gradient Vector Flow; GPU; OpenCL},
+author={Smistad, Erik and Elster, AnneC. and Lindseth, Frank},
+pages={1-8},
+language={English}
+}
+@inproceedings{bauer2009segmentation,
+  title={Segmentation of airways based on gradient vector flow},
+  author={Bauer, Christian and Bischof, Horst and Beichel, Reinhard},
+  booktitle={International workshop on pulmonary image analysis, Medical image computing and computer assisted intervention},
+  pages={191--201},
+  year={2009},
+}
+@article{zheng2012fast,
+  title={A Fast GVF Snake Algorithm on the GPU},
+  author={Zheng, Zuoyong and Zhang, Ruixia},
+  journal={image},
+  volume={2},
+  pages={4},
+  year={2012}
+}
+@INPROCEEDINGS{5459410, 
+author={Catanzaro, Bryan and Bor-Yiing Su and Sundaram, N. and Yunsup Lee and Murphy, Mark and Keutzer, K.}, 
+booktitle={Computer Vision, 2009 IEEE 12th International Conference on}, 
+title={Efficient, high-quality image contour detection}, 
+year={2009}, 
+pages={2381-2388}, 
+keywords={Application software;Computer vision;Detection algorithms;Detectors;Image analysis;Image segmentation;Object detection;Object recognition;Parallel algorithms;Runtime}, 
+doi={10.1109/ICCV.2009.5459410}, 
+ISSN={1550-5499},}
+@article{bresenham1965algorithm,
+  title={Algorithm for computer control of a digital plotter},
+  author={Bresenham, Jack E},
+  journal={IBM Systems journal},
+  volume={4},
+  number={1},
+  pages={25--30},
+  year={1965},
+  publisher={IBM}
+}
+@inproceedings{martin2001database,
+  title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
+  author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
+  booktitle={Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on},
+  volume={2},
+  pages={416--423},
+  year={2001},
+  organization={IEEE}
+}
\ No newline at end of file

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+\citation{snakegvf06}
+\citation{bauer2009segmentation}
+\citation{li2011robust}
+\citation{snakegvfopencl12}
+\citation{snakegvf06}
+\citation{zheng2012fast}

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+\@writefile{toc}{\contentsline {chapter}{\numberline {3}La segmentation orient\IeC {\'e}e r\IeC {\'e}gions dans les images bruit\IeC {\'e}es}{39}{chapter.3}}

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@@ -293,86 +310,95 @@
 \bibcite{aldinucci2012parallel}{5}
 \bibcite{arbelaez2011contour}{6}
 \bibcite{arora1998approximation}{7}
 \bibcite{aldinucci2012parallel}{5}
 \bibcite{arbelaez2011contour}{6}
 \bibcite{arora1998approximation}{7}

-\bibcite{bertaux2004speckle}{8}
-\bibcite{boykov2004experimental}{9}
-\bibcite{1467423}{10}
-\bibcite{BuadesCM06}{11}
-\bibcite{Caselles99topographicmaps}{12}
-\bibcite{cates2004gist}{13}
-\bibcite{chandran2009computational}{14}
-\bibcite{che2008performance}{15}
-\bibcite{chen09}{16}
-\bibcite{1093941}{17}
-\bibcite{cheng1995mean}{18}
-\bibcite{cherkassky1997implementing}{19}
-\bibcite{ChesnaudRB99}{20}
-\bibcite{cohen1993surface}{21}
-\bibcite{comaniciu1999mean}{22}
-\bibcite{comaniciu2002mean}{23}
-\bibcite{cutrona1990synthetic}{24}
-\bibcite{Dabov06imagedenoising}{25}
-\bibcite{Dabov09bm3dimage}{26}
-\bibcite{Daubechies:1992:TLW:130655}{27}
-\bibcite{dixit2005gpu}{28}
-\bibcite{elad2006image}{29}
-\bibcite{felzenszwalb2004efficient}{30}
-\bibcite{fluck2006gpu}{31}
-\bibcite{foley1994introduction}{32}
-\bibcite{ford1955simple}{33}
-\bibcite{fukunaga1975estimation}{34}
-\bibcite{fulkerson2012really}{35}
-\bibcite{GallandBR03}{36}
-\bibcite{GermainR01}{37}
-\bibcite{nlmeansgpubelge}{38}
-\bibcite{healey1994radiometric}{39}
-\bibcite{hochbaum2013simplifications}{40}
-\bibcite{5170921}{41}
-\bibcite{humphrey1924psychology}{42}
-\bibcite{jeong2009scalable}{43}
-\bibcite{5402362}{44}
-\bibcite{KassWT88}{45}
-\bibcite{keselman1998extraction}{46}
-\bibcite{cmla2009Kes}{47}
-\bibcite{kohli2007dynamic}{48}
-\bibcite{lefohn2003inter}{49}
-\bibcite{lefohn2003interactive}{50}
-\bibcite{lefohn2005streaming}{51}
-\bibcite{li2009mean}{52}
-\bibcite{macqueen1967some}{53}
-\bibcite{Mallat:2008:WTS:1525499}{54}
-\bibcite{mancuso2001introduction}{55}
-\bibcite{coil}{56}
-\bibcite{osher1988fronts}{57}
-\bibcite{4310076}{58}
-\bibcite{PALHANOXAVIERDEFONTES}{59}
-\bibcite{pelleg2000x}{60}
-\bibcite{4287006}{61}
-\bibcite{1521458}{62}
-\bibcite{4587843}{63}
-\bibcite{Roberts:2010:WGA:1921479.1921499}{64}
-\bibcite{ronfard1994region}{65}
-\bibcite{rumpf2001level}{66}
-\bibcite{6288187}{67}
-\bibcite{sethian1996fast}{68}
-\bibcite{kmeansgpuopengl}{69}
-\bibcite{shi2000normalized}{70}
-\bibcite{convolutionsoup}{71}
-\bibcite{graphcutscuda}{72}
-\bibcite{strang1999discrete}{73}
-\bibcite{theuwissen2001ccd}{74}
-\bibcite{710815}{75}
-\bibcite{tukey77}{76}
-\bibcite{vedaldi2008quick}{77}
-\bibcite{4563095}{78}
-\bibcite{Vineet:2009:FMS:1572769.1572796}{79}
-\bibcite{wang2001image}{80}
-\bibcite{wang2003image}{81}
-\bibcite{Wang04imagequality}{82}
-\bibcite{wu1993optimal}{83}
-\bibcite{xiao2010efficient}{84}
-\bibcite{5206542}{85}
-\bibcite{Zahn:1971:GMD:1309266.1309359}{86}
-\bibcite{zheng2011performance}{87}
+\bibcite{bauer2009segmentation}{8}
+\bibcite{bertaux2004speckle}{9}
+\bibcite{boykov2004experimental}{10}
+\bibcite{bresenham1965algorithm}{11}
+\bibcite{1467423}{12}
+\bibcite{BuadesCM06}{13}
+\bibcite{Caselles99topographicmaps}{14}
+\bibcite{5459410}{15}
+\bibcite{cates2004gist}{16}
+\bibcite{chandran2009computational}{17}
+\bibcite{che2008performance}{18}
+\bibcite{chen09}{19}
+\bibcite{1093941}{20}
+\bibcite{cheng1995mean}{21}
+\bibcite{cherkassky1997implementing}{22}
+\bibcite{ChesnaudRB99}{23}
+\bibcite{cohen1993surface}{24}
+\bibcite{comaniciu1999mean}{25}
+\bibcite{comaniciu2002mean}{26}
+\bibcite{cutrona1990synthetic}{27}
+\bibcite{Dabov06imagedenoising}{28}
+\bibcite{Dabov09bm3dimage}{29}
+\bibcite{Daubechies:1992:TLW:130655}{30}
+\bibcite{dixit2005gpu}{31}
+\bibcite{elad2006image}{32}
+\bibcite{felzenszwalb2004efficient}{33}
+\bibcite{fluck2006gpu}{34}
+\bibcite{foley1994introduction}{35}
+\bibcite{ford1955simple}{36}
+\bibcite{fukunaga1975estimation}{37}
+\bibcite{fulkerson2012really}{38}
+\bibcite{GallandBR03}{39}
+\bibcite{GermainR01}{40}
+\bibcite{nlmeansgpubelge}{41}
+\bibcite{snakegvf06}{42}
+\bibcite{healey1994radiometric}{43}
+\bibcite{hochbaum2013simplifications}{44}
+\bibcite{5170921}{45}
+\bibcite{humphrey1924psychology}{46}
+\bibcite{jeong2009scalable}{47}
+\bibcite{5402362}{48}
+\bibcite{KassWT88}{49}
+\bibcite{keselman1998extraction}{50}
+\bibcite{cmla2009Kes}{51}
+\bibcite{kohli2007dynamic}{52}
+\bibcite{lefohn2003inter}{53}
+\bibcite{lefohn2003interactive}{54}
+\bibcite{lefohn2005streaming}{55}
+\bibcite{li2009mean}{56}
+\bibcite{li2011robust}{57}
+\bibcite{macqueen1967some}{58}
+\bibcite{Mallat:2008:WTS:1525499}{59}
+\bibcite{mancuso2001introduction}{60}
+\bibcite{martin2001database}{61}
+\bibcite{coil}{62}
+\bibcite{osher1988fronts}{63}
+\bibcite{4310076}{64}
+\bibcite{PALHANOXAVIERDEFONTES}{65}
+\bibcite{pelleg2000x}{66}
+\bibcite{4287006}{67}
+\bibcite{1521458}{68}
+\bibcite{4587843}{69}
+\bibcite{Roberts:2010:WGA:1921479.1921499}{70}
+\bibcite{ronfard1994region}{71}
+\bibcite{rumpf2001level}{72}
+\bibcite{6288187}{73}
+\bibcite{sethian1996fast}{74}
+\bibcite{kmeansgpuopengl}{75}
+\bibcite{shi2000normalized}{76}
+\bibcite{snakegvfopencl12}{77}
+\bibcite{convolutionsoup}{78}
+\bibcite{graphcutscuda}{79}
+\bibcite{strang1999discrete}{80}
+\bibcite{theuwissen2001ccd}{81}
+\bibcite{710815}{82}
+\bibcite{tukey77}{83}
+\bibcite{vedaldi2008quick}{84}
+\bibcite{4563095}{85}
+\bibcite{Vineet:2009:FMS:1572769.1572796}{86}
+\bibcite{wang2001image}{87}
+\bibcite{wang2003image}{88}
+\bibcite{Wang04imagequality}{89}
+\bibcite{wu1993optimal}{90}
+\bibcite{xiao2010efficient}{91}
+\bibcite{5206542}{92}
+\bibcite{Zahn:1971:GMD:1309266.1309359}{93}
+\bibcite{zheng2011performance}{94}
+\bibcite{zheng2012fast}{95}

 \citation{zheng2011performance}
 \citation{graphcutscuda}
 \citation{fulkerson2012really}
 \citation{zheng2011performance}
 \citation{graphcutscuda}
 \citation{fulkerson2012really}

+\citation{martin2001database}

diff --git a/THESE/these.bbl b/THESE/these.bbl
index 851ac749426b69084fe3b95f76e3b4f284ac9da7..1dd0d5a101f625ae4e6b0606dd3cfb470ede6d2a 100644 (file)
--- a/THESE/these.bbl
+++ b/THESE/these.bbl
@@ -35,6 +35,12 @@ Sanjeev Arora, Prabhakar Raghavan, and Satish Rao.
 \newblock In {\em Proceedings of the thirtieth annual ACM symposium on Theory
   of computing}, pages 106--113. ACM, 1998.
 
 \newblock In {\em Proceedings of the thirtieth annual ACM symposium on Theory
   of computing}, pages 106--113. ACM, 1998.
 

+\bibitem{bauer2009segmentation}
+Christian Bauer, Horst Bischof, and Reinhard Beichel.
+\newblock Segmentation of airways based on gradient vector flow.
+\newblock In {\em International workshop on pulmonary image analysis, Medical
+  image computing and computer assisted intervention}, pages 191--201, 2009.
+

 \bibitem{bertaux2004speckle}
 Nicolas Bertaux, Yann Frauel, Philippe R{\'e}fr{\'e}gier, and Bahram Javidi.
 \newblock Speckle removal using a maximum-likelihood technique with isoline
 \bibitem{bertaux2004speckle}
 Nicolas Bertaux, Yann Frauel, Philippe R{\'e}fr{\'e}gier, and Bahram Javidi.
 \newblock Speckle removal using a maximum-likelihood technique with isoline


@@ -48,6 +54,11 @@ Yuri Boykov and Vladimir Kolmogorov.
 \newblock {\em Pattern Analysis and Machine Intelligence, IEEE Transactions
   on}, 26(9):1124--1137, 2004.
 
 \newblock {\em Pattern Analysis and Machine Intelligence, IEEE Transactions
   on}, 26(9):1124--1137, 2004.
 

+\bibitem{bresenham1965algorithm}
+Jack~E Bresenham.
+\newblock Algorithm for computer control of a digital plotter.
+\newblock {\em IBM Systems journal}, 4(1):25--30, 1965.
+

 \bibitem{1467423}
 A.~Buades, B.~Coll, and J.~M Morel.
 \newblock A non-local algorithm for image denoising.
 \bibitem{1467423}
 A.~Buades, B.~Coll, and J.~M Morel.
 \newblock A non-local algorithm for image denoising.


@@ -64,6 +75,13 @@ Vicent Caselles and Jean michel Morel.
 \newblock Topographic maps and local contrast changes in natural images.
 \newblock {\em Int. J. Comp. Vision}, 33:5--27, 1999.
 
 \newblock Topographic maps and local contrast changes in natural images.
 \newblock {\em Int. J. Comp. Vision}, 33:5--27, 1999.
 

+\bibitem{5459410}
+Bryan Catanzaro, Bor-Yiing Su, N.~Sundaram, Yunsup Lee, Mark Murphy, and
+  K.~Keutzer.
+\newblock Efficient, high-quality image contour detection.
+\newblock In {\em Computer Vision, 2009 IEEE 12th International Conference on},
+  pages 2381--2388, 2009.
+

 \bibitem{cates2004gist}
 Joshua~E Cates, Aaron~E Lefohn, and Ross~T Whitaker.
 \newblock Gist: an interactive, gpu-based level set segmentation tool for 3d
 \bibitem{cates2004gist}
 Joshua~E Cates, Aaron~E Lefohn, and Ross~T Whitaker.
 \newblock Gist: an interactive, gpu-based level set segmentation tool for 3d


@@ -224,6 +242,15 @@ Bart Goossens, Hiêp Luong, Jan Aelterman, Aleksandra Pižurica, and Wilfried
   Systems}, volume 6475 of {\em Lecture Notes in Computer Science}, pages
   46--57. Springer Berlin Heidelberg, 2010.
 
   Systems}, volume 6475 of {\em Lecture Notes in Computer Science}, pages
   46--57. Springer Berlin Heidelberg, 2010.
 

+\bibitem{snakegvf06}
+Zhiyu He and Falko Kuester.
+\newblock Gpu-based active contour segmentation using gradient vector flow.
+\newblock In George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Paolo
+  Remagnino, Ara Nefian, Gopi Meenakshisundaram, Valerio Pascucci, Jiri Zara,
+  Jose Molineros, Holger Theisel, and Tom Malzbender, editors, {\em Advances in
+  Visual Computing}, volume 4291 of {\em Lecture Notes in Computer Science},
+  pages 191--201. Springer Berlin Heidelberg, 2006.
+

 \bibitem{healey1994radiometric}
 Glenn~E Healey and Raghava Kondepudy.
 \newblock Radiometric ccd camera calibration and noise estimation.
 \bibitem{healey1994radiometric}
 Glenn~E Healey and Raghava Kondepudy.
 \newblock Radiometric ccd camera calibration and noise estimation.


@@ -309,6 +336,12 @@ Peihua Li and Lijuan Xiao.
 \newblock In {\em Pattern Recognition and Image Analysis}, pages 120--127.
   Springer, 2009.
 
 \newblock In {\em Pattern Recognition and Image Analysis}, pages 120--127.
   Springer, 2009.
 

+\bibitem{li2011robust}
+Tao Li, Alexandre Krupa, and Christophe Collewet.
+\newblock A robust parametric active contour based on fourier descriptors.
+\newblock In {\em Image Processing (ICIP), 2011 18th IEEE International
+  Conference on}, pages 1037--1040. IEEE, 2011.
+

 \bibitem{macqueen1967some}
 James MacQueen et~al.
 \newblock Some methods for classification and analysis of multivariate
 \bibitem{macqueen1967some}
 James MacQueen et~al.
 \newblock Some methods for classification and analysis of multivariate


@@ -327,6 +360,13 @@ Massimo Mancuso and Sebastiano Battiato.
 \newblock An introduction to the digital still camera technology.
 \newblock {\em ST Journal of System Research}, 2(2), 2001.
 
 \newblock An introduction to the digital still camera technology.
 \newblock {\em ST Journal of System Research}, 2(2), 2001.
 

+\bibitem{martin2001database}
+David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik.
+\newblock A database of human segmented natural images and its application to
+  evaluating segmentation algorithms and measuring ecological statistics.
+\newblock In {\em Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE
+  International Conference on}, volume~2, pages 416--423. IEEE, 2001.
+

 \bibitem{coil}
 S.A. Nene, S.K. Nayar, and Murase H.
 \newblock Columbia object image library (coil-100).
 \bibitem{coil}
 S.A. Nene, S.K. Nayar, and Murase H.
 \newblock Columbia object image library (coil-100).


@@ -418,6 +458,11 @@ Jianbo Shi and Jitendra Malik.
 \newblock {\em Pattern Analysis and Machine Intelligence, IEEE Transactions
   on}, 22(8):888--905, 2000.
 
 \newblock {\em Pattern Analysis and Machine Intelligence, IEEE Transactions
   on}, 22(8):888--905, 2000.
 

+\bibitem{snakegvfopencl12}
+Erik Smistad, AnneC. Elster, and Frank Lindseth.
+\newblock Real-time gradient vector flow on gpus using opencl.
+\newblock {\em Journal of Real-Time Image Processing}, pages 1--8, 2012.
+

 \bibitem{convolutionsoup}
 J.~Stam.
 \newblock Convolution soup.
 \bibitem{convolutionsoup}
 J.~Stam.
 \newblock Convolution soup.


@@ -519,4 +564,9 @@ Z.~Zheng, W.~Xu, and K.~Mueller.
 \newblock {\em Workshop on High Performance Image Reconstruction (HPIR)}, pages
   52--55, 2011.
 
 \newblock {\em Workshop on High Performance Image Reconstruction (HPIR)}, pages
   52--55, 2011.
 

+\bibitem{zheng2012fast}
+Zuoyong Zheng and Ruixia Zhang.
+\newblock A fast gvf snake algorithm on the gpu.
+\newblock {\em image}, 2:4, 2012.
+

 \end{thebibliography}
 \end{thebibliography}

diff --git a/THESE/these.blg b/THESE/these.blg
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--- a/THESE/these.blg
+++ b/THESE/these.blg
@@ -10,45 +10,45 @@ Warning--empty journal in cohen1993surface
 Warning--empty journal in dixit2005gpu
 Warning--can't use both volume and number fields in macqueen1967some
 Warning--can't use both volume and number fields in xiao2010efficient
 Warning--empty journal in dixit2005gpu
 Warning--can't use both volume and number fields in macqueen1967some
 Warning--can't use both volume and number fields in xiao2010efficient

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diff --git a/THESE/these.lof b/THESE/these.lof
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+++ b/THESE/these.lof
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 \contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Cerveau 256$\times $256$\times $256 en 7~s}}}{36}{figure.2.17}
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 \contentsline {figure}{\numberline {2.17}{\ignorespaces Segmentation d'images issues d'examens IRM par la m\IeC {\'e}thode des level set \IeC {\`a} bande \IeC {\'e}troite.}}{36}{figure.2.17}
 \contentsline {subfigure}{\numberline {(a)}{\ignorespaces {Cerveau 256$\times $256$\times $256 en 7~s}}}{36}{figure.2.17}
 \contentsline {subfigure}{\numberline {(b)}{\ignorespaces {Reins et aorte, 256$\times $256$\times $272 en 16~s}}}{36}{figure.2.17}

+\contentsline {figure}{\numberline {2.18}{\ignorespaces Extraction de contour par la version GPU de l'algorithme gPb. Les images sont issues de la base BSDS \cite {martin2001database}}}{38}{figure.2.18}

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@@ -1051,12 +1048,12 @@ Chapitre 2.
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 File: /home/zulu/Documents/these_gilles/THESE/codes/seg_histogramme/cochon256-s
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 File: /home/zulu/Documents/these_gilles/THESE/codes/snake/cochon128_tradi_snake
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 Chapitre 3.
 Chapitre 3.

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diff --git a/THESE/these.out b/THESE/these.out
index 666a7ec09e9b00184f10558882e9de60aba13ff3..69afe0e88aa22308f46e9e041d145aa705f964ed 100644 (file)
--- a/THESE/these.out
+++ b/THESE/these.out
@@ -29,6 +29,7 @@
 \BOOKMARK [2][]{subsection.2.6.2}{2.6.2 Partitionnement de graphe}{section.2.6}
 \BOOKMARK [2][]{subsection.2.6.3}{2.6.3 K-means, mean-shift et apparent\351s}{section.2.6}
 \BOOKMARK [2][]{subsection.2.6.4}{2.6.4 Snakes et Level set}{section.2.6}
 \BOOKMARK [2][]{subsection.2.6.2}{2.6.2 Partitionnement de graphe}{section.2.6}
 \BOOKMARK [2][]{subsection.2.6.3}{2.6.3 K-means, mean-shift et apparent\351s}{section.2.6}
 \BOOKMARK [2][]{subsection.2.6.4}{2.6.4 Snakes et Level set}{section.2.6}

+\BOOKMARK [2][]{subsection.2.6.5}{2.6.5 Algorithmes hybrides}{section.2.6}

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diff --git a/THESE/these.tex b/THESE/these.tex
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diff --git a/THESE/these.toc b/THESE/these.toc
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 \contentsline {subsection}{\numberline {2.6.2}Partitionnement de graphe}{32}{subsection.2.6.2}
 \contentsline {subsection}{\numberline {2.6.3}K-means, mean-shift et apparent\IeC {\'e}s}{32}{subsection.2.6.3}
 \contentsline {subsection}{\numberline {2.6.4}Snakes et Level set}{34}{subsection.2.6.4}
 \contentsline {subsection}{\numberline {2.6.2}Partitionnement de graphe}{32}{subsection.2.6.2}
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-\contentsline {chapter}{\numberline {3}La segmentation orient\IeC {\'e}e r\IeC {\'e}gions dans les images bruit\IeC {\'e}es}{37}{chapter.3}
-\contentsline {section}{\numberline {3.1}Pr\IeC {\'e}sentation - existant}{37}{section.3.1}
-\contentsline {section}{\numberline {3.2}La parall\IeC {\`e}lisation du snake polygonal}{37}{section.3.2}
-\contentsline {chapter}{\numberline {4}Le filtrage des images sur GPU}{39}{chapter.4}
-\contentsline {section}{\numberline {4.1}Algorithme de r\IeC {\'e}duction de bruit par recherche des lignes de niveaux}{39}{section.4.1}
-\contentsline {section}{\numberline {4.2}Filtre m\IeC {\'e}dian}{39}{section.4.2}
-\contentsline {section}{\numberline {4.3}Filtres de convolution}{39}{section.4.3}
-\contentsline {chapter}{\numberline {5}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{41}{chapter.5}
+\contentsline {subsection}{\numberline {2.6.5}Algorithmes hybrides}{37}{subsection.2.6.5}
+\contentsline {chapter}{\numberline {3}La segmentation orient\IeC {\'e}e r\IeC {\'e}gions dans les images bruit\IeC {\'e}es}{39}{chapter.3}
+\contentsline {section}{\numberline {3.1}Pr\IeC {\'e}sentation - existant}{39}{section.3.1}
+\contentsline {section}{\numberline {3.2}La parall\IeC {\`e}lisation du snake polygonal}{39}{section.3.2}
+\contentsline {chapter}{\numberline {4}Le filtrage des images sur GPU}{41}{chapter.4}
+\contentsline {section}{\numberline {4.1}Algorithme de r\IeC {\'e}duction de bruit par recherche des lignes de niveaux}{41}{section.4.1}
+\contentsline {section}{\numberline {4.2}Filtre m\IeC {\'e}dian}{41}{section.4.2}
+\contentsline {section}{\numberline {4.3}Filtres de convolution}{41}{section.4.3}
+\contentsline {chapter}{\numberline {5}Conclusion g\IeC {\'e}n\IeC {\'e}rale}{43}{chapter.5}