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Artykuły w czasopismach na temat "Chaînes de Markov triplet"
Pieczynski, Wojciech. "Copules gaussiennes dans les chaînes triplet partiellement de Markov". Comptes Rendus Mathematique 341, nr 3 (sierpień 2005): 189–94. http://dx.doi.org/10.1016/j.crma.2005.06.012.
Pełny tekst źródłaPieczynski, Wojciech. "Fusion de Dempster–Shafer dans les chaînes triplet partiellement de Markov". Comptes Rendus Mathematique 339, nr 11 (grudzień 2004): 797–802. http://dx.doi.org/10.1016/j.crma.2004.10.013.
Pełny tekst źródłaDelmotte, Thierry. "Estimations pour les chaînes de Markov réversibles". Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 324, nr 9 (maj 1997): 1053–58. http://dx.doi.org/10.1016/s0764-4442(97)87885-8.
Pełny tekst źródłaBrossard, Jean, i Christophe Leuridan. "Chaînes de Markov Constructives Indexées par Z". Annals of Probability 35, nr 2 (marzec 2007): 715–31. http://dx.doi.org/10.1214/009117906000000430.
Pełny tekst źródłaEl Yazid Boudaren, Mohamed, Emmanuel Monfrini, Wojciech Pieczynski i Amar Aissani. "Phasic Triplet Markov Chains". IEEE Transactions on Pattern Analysis and Machine Intelligence 36, nr 11 (1.11.2014): 2310–16. http://dx.doi.org/10.1109/tpami.2014.2327974.
Pełny tekst źródłaPieczynski, Wojciech. "Chaı̂nes de Markov Triplet". Comptes Rendus Mathematique 335, nr 3 (styczeń 2002): 275–78. http://dx.doi.org/10.1016/s1631-073x(02)02462-7.
Pełny tekst źródłaCourbot, Jean-Baptiste, Emmanuel Monfrini, Vincent Mazet i Christophe Collet. "Oriented Triplet Markov Fields". Pattern Recognition Letters 103 (luty 2018): 16–22. http://dx.doi.org/10.1016/j.patrec.2017.12.026.
Pełny tekst źródłaLadet, Sylvie, Marc Deconchat, Claude Monteil, Jean-Paul Lacombe i Gérard Ballent. "Les chaînes de Markov spatialisées comme outil de simulation". Revue internationale de géomatique 15, nr 2 (30.06.2005): 159–73. http://dx.doi.org/10.3166/rig.15.159-173.
Pełny tekst źródłaAit-El-Fquih, B., i F. Desbouvries. "Kalman Filtering in Triplet Markov Chains". IEEE Transactions on Signal Processing 54, nr 8 (sierpień 2006): 2957–63. http://dx.doi.org/10.1109/tsp.2006.877651.
Pełny tekst źródłaMiclo, Laurent. "Une variante de l'inégalité de Cheeger pour les chaînes de Markov finies". ESAIM: Probability and Statistics 2 (1998): 1–21. http://dx.doi.org/10.1051/ps:1998101.
Pełny tekst źródłaRozprawy doktorskie na temat "Chaînes de Markov triplet"
Ben, Mabrouk Mohamed. "Modèles de Markov triplets en restauration des signaux". Phd thesis, Institut National des Télécommunications, 2011. http://tel.archives-ouvertes.fr/tel-00694128.
Pełny tekst źródłaFernandes, Clément. "Chaînes de Markov triplets et segmentation non supervisée d'images". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS019.
Pełny tekst źródłaHidden Markov chains (HMC) are widely used in unsupervised Bayesian hidden discrete data restoration. They are very robust and, in spite of their simplicity, they are sufficiently efficient in many situations. In particular for image segmentation, despite their mono-dimensional nature, they are able, through a transformation of the bi-dimensional images into mono-dimensional sequences with Peano scan (PS), to give satisfying results. However, sometimes, more complex models such as hidden Markov fields (HMF) may be preferred in spite of their increased time complexity, for their better results. Moreover, hidden Markov models (the chains as well as the fields) have been extended to pairwise and triplet Markov models, which can be of interest in more complex situations. For example, when sojourn time in hidden states is not geometrical, hidden semi-Markov (HSMC) chains tend to perform better than HMC, and such is also the case for hidden evidential Markov chains (HEMC) when data are non-stationary. In this thesis, we first propose a new triplet Markov chain (TMC), which simultaneously extends HSMC and HEMC. Based on hidden triplet Markov chains (HTMC), the new hidden evidential semi-Markov chain (HESMC) model can be used in unsupervised framework, parameters being estimated with Expectation-Maximization (EM) algorithm. We validate its interest through some experiments on synthetic data. Then we address the problem of mono-dimensionality of the HMC with PS model in image segmentation by introducing the “contextual” Peano scan (CPS). It consists in associating to each index in the HMC obtained from PS, two observations on pixels which are neighbors of the pixel considered in the image, but are not its neighbors in the HMC. This gives three observations on each point of the Peano scan, which leads to a new conditional Markov chain (CMC) with a more complex structure, but whose posterior law is still Markovian. Therefore, we can apply the usual parameter estimation method: Stochastic Expectation-Maximization (SEM), as well as study unsupervised segmentation Marginal Posterior Mode (MPM) so obtained. The CMC with CPS based supervised and unsupervised MPM are compared to the classic scan based HMC-PS and the HMF through experiments on artificial images. They improve notably the former, and can even compete with the latter. Finally, we extend the CMC-CPS to Pairwise Conditional Markov (CPMC) chains and two particular triplet conditional Markov chain: evidential conditional Markov chains (CEMC) and conditional semi-Markov chains (CSMC). For each of these extensions, we show through experiments on artificial images that these models can improve notably their non conditional counterpart, as well as the CMC with CPS, and can even compete with the HMF. Beside they allow the generality of markovian triplets to better play its part in image segmentation, while avoiding the substantial time complexity of triplet Markov fields
Rafi, Selwa. "Chaînes de Markov cachées et séparation non supervisée de sources". Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0020/document.
Pełny tekst źródłaThe restoration problem is usually encountered in various domains and in particular in signal and image processing. It consists in retrieving original data from a set of observed ones. For multidimensional data, the problem can be solved using different approaches depending on the data structure, the transformation system and the noise. In this work, we have first tackled the problem in the case of discrete data and noisy model. In this context, the problem is similar to a segmentation problem. We have exploited Pairwise and Triplet Markov chain models, which generalize Hidden Markov chain models. The interest of these models consist in the possibility to generalize the computation procedure of the posterior probability, allowing one to perform bayesian segmentation. We have considered these methods for two-dimensional signals and we have applied the algorithms to retrieve of old hand-written document which have been scanned and are subject to show through effect. In the second part of this work, we have considered the restoration problem as a blind source separation problem. The well-known "Independent Component Analysis" (ICA) method requires the assumption that the sources be statistically independent. In practice, this condition is not always verified. Consequently, we have studied an extension of the ICA model in the case where the sources are not necessarily independent. We have introduced a latent process which controls the dependence and/or independence of the sources. The model that we propose combines a linear instantaneous mixing model similar to the one of ICA model and a probabilistic model on the sources with hidden variables. In this context, we show how the usual independence assumption can be weakened using the technique of Iterative Conditional Estimation to a conditional independence assumption
Lanchantin, Pierre. "Chaînes de Markov triplets et segmentation non supervisée de signaux". Evry, Institut national des télécommunications, 2006. http://www.theses.fr/2006TELE0012.
Pełny tekst źródłaThe aim of this thesis is to propose original methods of unsupervised signal and image segmentation , based on triplet Markov and partially pairwise Markov models. We first describe different models with increasing generality and develop inference and parameters estimation algorithms in the monodimensional case ( chains). Then we propose and study particular cases of triplet partially Markov chains, starting with a model of pairwise partially Markov chains to the segmentation of centured gaussian processes with long correlation noise. The segmentation of centured gaussian processes with long correlation noise. Finally, we propose a triplet Markov chains model adapted to the segmentation of non stationary hidden processes. We also study the extension possibilites of classical probabilistic models ( chains and trees) in an evidential model, where the posterior hidden process distribution is given by the Dempster-Shafer fusion and in a "fuzzy "model in which the mebership function is fuzzy
Ait, El Fquih Boujemaa. "Estimation bayésienne non supervisée dans les chaînes de Markov triplets continues". Evry, Institut national des télécommunications, 2007. http://www.theses.fr/2007TELE0014.
Pełny tekst źródłaA triplet Markov chain (TMC) is a stochastic dynamical model in which the state x, the observation y, and a third process r jointly form a vectorial Markov chain. This model is a generalization of the classical hidden Markov chain (HMC) model. The work of this thesis is devoted to the restoration problem and the parameter estimation problem in continuous TMC model. We propose filtering and fixed-interval Bayesian smoothing algorithms. In the particular case of Gaussian TMC, some of these algorithms extend to the TMC framework some Kalman type filtering or smoothing algorithms previously derived in the state-space framework ; however some algorithms remain original. We also propose for the general case sequential Monte Carlo based restoration algorithms. Some of these algorithms extend to TMC particle filtering or smoothing algorithms which were originally introduced in non linear and/or non Gaussian state-space systems ; some other algorithms remain original. We next adress the unsupervised case and we propose an EM parameter estimation algorithm. We finally adress a blind turbo-equalization problem in the presence of Inter-Symbol Interferences. The proposed equalizer is a fixed-lag sequential Monte Carlo smoothing algorithm
Abbassi, Noufel. "Chaînes de Markov triplets et filtrage optimal dans les systemes à sauts". Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00873630.
Pełny tekst źródłaRAFI, Selwa. "Chaînes de Markov cachées et séparation non supervisée de sources". Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00995414.
Pełny tekst źródłaBoudaren, Mohamed El Yazid. "Modèles graphiques évidentiels". Phd thesis, Institut National des Télécommunications, 2014. http://tel.archives-ouvertes.fr/tel-01004504.
Pełny tekst źródłaBoudaren, Mohamed El Yazid. "Modèles graphiques évidentiels". Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2014. http://www.theses.fr/2014TELE0001.
Pełny tekst źródłaHidden Markov chains (HMCs) based approaches have been shown to be efficient to resolve a wide range of inverse problems occurring in image and signal processing. In particular, unsupervised segmentation of data is one of these problems where HMCs have been extensively applied. According to such models, the observed data are considered as a noised version of the requested segmentation that can be modeled through a finite Markov chain. Then, Bayesian techniques such as MPM can be applied to estimate this segmentation even in unsupervised way thanks to some algorithms that make it possible to estimate the model parameters from the only observed data. HMCs have then been generalized to pairwise Markov chains (PMCs) and triplet Markov chains (TMCs), which offer more modeling possibilities while showing comparable computational complexities, and thus, allow to consider some challenging situations that the conventional HMCs cannot support. An interesting link has also been established between the Dempster-Shafer theory of evidence and TMCs, which give to these latter the ability to handle multisensor data. Hence, in this thesis, we deal with three challenging difficulties that conventional HMCs cannot handle: nonstationarity of the a priori and/or noise distributions, noise correlation, multisensor information fusion. For this purpose, we propose some original models in accordance with the rich theory of TMCs. First, we introduce the M-stationary noise- HMC (also called jumping noise- HMC) that takes into account the nonstationary aspect of the noise distributions in an analogous manner with the switching-HMCs. Afterward, ML-stationary HMC consider nonstationarity of both the a priori and/or noise distributions. Second, we tackle the problem of non-stationary PMCs in two ways. In the Bayesian context, we define the M-stationary PMC and the MM-stationary PMC (also called switching PMCs) that partition the data into M stationary segments. In the evidential context, we propose the evidential PMC in which the realization of the hidden process is modeled through a mass function. Finally, we introduce the multisensor nonstationary HMCs in which the Dempster-Shafer fusion has been used on one hand, to model the data nonstationarity (as done in the hidden evidential Markov chains) and on the other hand, to fuse the information provided by the different sensors (as in the multisensor hidden Markov fields context). For each of the proposed models, we describe the associated segmentation and parameters estimation procedures. The interest of each model is also assessed, with respect to the former ones, through experiments conducted on synthetic and real data
Lapuyade-Lahorgue, Jérôme. "Sur diverses extensions des chaînes de Markov cachées avec application au traitement des signaux radar". Phd thesis, Institut National des Télécommunications, 2008. http://tel.archives-ouvertes.fr/tel-00473711.
Pełny tekst źródłaKsiążki na temat "Chaînes de Markov triplet"
Foata, Dominique. Processus stochastiques: Processus de Poisson, chaînes de Markov et martingales : cours et exercices corrigeś. Paris: Dunod, 2004.
Znajdź pełny tekst źródłaRobert, Christian. Méthodes de Monte Carlo par chaînes de Markov. Economica, 1996.
Znajdź pełny tekst źródłaCzęści książek na temat "Chaînes de Markov triplet"
Jedrzejewski, Franck. "Chaînes de Markov". W Modèles aléatoires et physique probabiliste, 67–88. Paris: Springer Paris, 2009. http://dx.doi.org/10.1007/978-2-287-99308-4_4.
Pełny tekst źródłaCaumel, Yves. "Chaînes de Markov discrètes". W Probabilités et processus stochastiques, 149–78. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0163-6_7.
Pełny tekst źródłaChafaï, Djalil, i Florent Malrieu. "Chaînes de Markov cachées". W Recueil de Modèles Aléatoires, 93–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49768-5_7.
Pełny tekst źródłaDel Moral, Pierre, i Christelle Vergé. "Chaînes de Markov Discrètes". W Mathématiques et Applications, 3–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54616-7_1.
Pełny tekst źródłaDel Moral, Pierre, i Christelle Vergé. "Chaînes de Markov Abstraites". W Mathématiques et Applications, 23–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54616-7_2.
Pełny tekst źródłaDel Moral, Pierre, i Christelle Vergé. "Chaînes de Markov Non Linéaires". W Mathématiques et Applications, 51–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54616-7_3.
Pełny tekst źródłaDel Moral, Pierre, i Christelle Vergé. "Chaînes de Markov en Auto-Interaction". W Mathématiques et Applications, 67–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54616-7_4.
Pełny tekst źródłaPieczynski, Wojciech. "Triplet Markov Chains and Image Segmentation". W Inverse Problems in Vision and 3D Tomography, 123–53. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118603864.ch4.
Pełny tekst źródłaChafaï, Djalil, i Florent Malrieu. "Des chaînes de Markov aux processus de diffusion". W Recueil de Modèles Aléatoires, 357–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49768-5_27.
Pełny tekst źródłaCaumel, Yves. "Chaînes de Markov à temps continu et files d’attente". W Probabilités et processus stochastiques, 203–33. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0163-6_9.
Pełny tekst źródłaStreszczenia konferencji na temat "Chaînes de Markov triplet"
Ameur, Meryem, Najlae Idrissi i Cherki Daoui. "Triplet Markov chain in images segmentation". W 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2018. http://dx.doi.org/10.1109/isacv.2018.8354055.
Pełny tekst źródłaCourbot, Jean-Baptiste, Emmanuel Monfrini, Vincent Mazet i Christophe Collet. "Triplet Markov Trees for Image Segmentation". W 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450841.
Pełny tekst źródłaPieczynski, Wojciech, Dalila Benboudjema i Pierre Lanchantin. "Statistical image segmentation using triplet Markov fields". W International Symposium on Remote Sensing, redaktor Sebastiano B. Serpico. SPIE, 2003. http://dx.doi.org/10.1117/12.463182.
Pełny tekst źródłaPieczynski, Wojciech, Cedric Hulard i Thomas Veit. "Triplet Markov chains in hidden signal restoration". W International Symposium on Remote Sensing, redaktor Sebastiano B. Serpico. SPIE, 2003. http://dx.doi.org/10.1117/12.463183.
Pełny tekst źródłaCourbot, Jean-Baptiste, Emmanuel Monfrini, Vincent Mazet i Christophe Collet. "Oriented Triplet Markov fields for hyperspectral image segmentation". W 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2016. http://dx.doi.org/10.1109/whispers.2016.8071755.
Pełny tekst źródłaGangloff, Hugo, Jean-Baptiste Courbot, Emmanuel Monfrini i Christophe Collet. "Unsupervised Image Segmentation with Spatial Triplet Markov Trees". W ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414435.
Pełny tekst źródłaBenboudjema, Dalila, Nadia Othman, Bernadette Dorizzi i Wojciech Pieczynski. "Challenging eye segmentation using Triplet Markov spatial models". W ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6637989.
Pełny tekst źródłaBenboudjema, D., i W. Pieczy. "Segmenting non stationary images with triplet Markov fields". W 2005 International Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1529751.
Pełny tekst źródłaPetetin, Yohan, i Francois Desbouvries. "Exact Bayesian estimation in constrained Triplet Markov Chains". W 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958847.
Pełny tekst źródłaBoudaren, Mohamed El Yazid, Emmanuel Monfrini, Kadda Beghdad Bey, Ahmed Habbouchi i Wojciech Pieczynski. "Unsupervised Segmentation of Nonstationary Data using Triplet Markov Chains". W 19th International Conference on Enterprise Information Systems. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006276704050414.
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