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Artykuły w czasopismach na temat "Segmentation non supervisée d'images"
Cheriguene, Rabia Sarah, i Habib Mahi. "Comparaison entre les méthodes J-SEG et MEANSHIFT : application sur des données THRS". Revue Française de Photogrammétrie et de Télédétection, nr 203 (8.04.2014): 27–32. http://dx.doi.org/10.52638/rfpt.2013.27.
Pełny tekst źródłaBenmostefa, Soumia, i Hadria Fizazi. "Classification automatique des images satellitaires optimisée par l'algorithme des chauves-souris". Revue Française de Photogrammétrie et de Télédétection, nr 203 (8.04.2014): 11–17. http://dx.doi.org/10.52638/rfpt.2013.25.
Pełny tekst źródłaTochon, Guillaume, Jean-Baptiste Féret, Silvia Valero, Roberta E. Martin, Raul Tupayachi, Jocelyn Chanussot, Philippe Salembier i Gregory P. Asner. "Segmentation hyperspectrale de forêts tropicales par Arbres de Partition Binaires". Revue Française de Photogrammétrie et de Télédétection, nr 202 (16.04.2014): 55–65. http://dx.doi.org/10.52638/rfpt.2013.51.
Pełny tekst źródłaOhmaid, Hicham, S. Eddarouich, A. Bourouhou i M. Timouya. "Comparison between SVM and KNN classifiers for iris recognition using a new unsupervised neural approach in segmentation". IAES International Journal of Artificial Intelligence (IJ-AI) 9, nr 3 (1.09.2020): 429. http://dx.doi.org/10.11591/ijai.v9.i3.pp429-438.
Pełny tekst źródłaHamzaouil, H., A. Elmatouat i P. Martin. "Segmentation d'une image couleur par les critères d'information et la théorie des ensembles flous". Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées Volume 5, Special Issue TAM... (9.08.2006). http://dx.doi.org/10.46298/arima.1861.
Pełny tekst źródłaRozprawy doktorskie na temat "Segmentation non supervisée d'images"
Fernandes, 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
Benboudjema, Dalila. "Champs de Markov triplets et segmentation bayésienne non supervisée d'images". Evry, Institut national des télécommunications, 2005. http://www.theses.fr/2005TELE0009.
Pełny tekst źródłaImage segmentation is a fundamental and yet difficult task in machine vision. Several models and approaches have been proposed, and the ones which have probably received considerable attention are hidden Markov fields (HMF) models. In such model the hidden field X which is assumed Markovian, must be estimated from the observed –or noisy- field Y. Such processing is possible because the distribution X conditional on the observed process Y remains markovian. This model has been generalized to the Pairwise Markov field (PMF) which offer similar processing and superior modelling capabilities. In this model we assume directly the markovianity of the couple (X,Y ). Afterwards, triplet Markov fields (TMF) which are the generalization of the PMF, have been proposed. In such model the distribution of the couple (X ,Y ) is the marginal distribution of a Markov field T = (X ,U,Y ) , where U is latent process. The aim of this thesis is to study the TMF models. Two original models are presented: the Evidential Markov field (EMF) allowing to model the evidential aspects of the prior information and the adapted triplet Markov field (ATMF), allowing to model the simultaneous presence of different stationarities in the class image. For the unsupervised processing, two original approaches of estimation the model’s parameters have been proposed. The first one is based on the stochastic gradient and the second one is based on the iterative conditional estimation (ICE) and the least square method, as well. The latter, have then been generalized to the non stationary images with non Gaussian correlated noise, which uses the Pearson system to find the natures of margins of the noise, which can vary with the class. Experiments indicate that the new models and related processing algorithms can improve the results obtained with the classical ones
Fontaine, Michaël. "Segmentation non supervisée d'images couleur par analyse de la connexité des pixels". Lille 1, 2001. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/2001/50376-2001-305-306.pdf.
Pełny tekst źródłaPeng, Anrong. "Segmentation statistique non supervisée d'images et de détection de contours par filtrage". Compiègne, 1992. http://www.theses.fr/1992COMPD512.
Pełny tekst źródłaEl, Asmar Saadallah. "Contributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques". Thesis, La Rochelle, 2016. http://www.theses.fr/2016LAROS023/document.
Pełny tekst źródłaHyperspectral images provided by modern spectrometers are composed of reflectance values at hundreds of narrow spectral bands covering a wide range of the electromagnetic spectrum. Since spectral reflectance differs for most of the materials or objects present in a given scene, hyperspectral image processing and analysis find many real-life applications. We address in this work the problem of unsupervised hyperspectral image segmentation following three distinct approaches. The first one is of Graph Embedding type and necessitates two steps : first, pixels of the original image patchs are compared using a spectral similarity measure and then objects obtained by local segmentations are fusioned by means of a similarity measure between objects. The second one is of Spectral Hashing or Semantic Hashing type. We first define a binary encoding of spectral variations and then propose a clustering segmentation relying on a k- mode classification algorithm adapted to the categorical nature of the data, the chosen distance being a generalized version of the classical Hamming distance. In the third one, we take advantage of the geometric information given by the manifolds associated to the images. Using the metric properties of the space of Riemannian metrics, that is the space of symmetric positive definite matrices, endowed with the so-called Fisher Rao metric, we propose a k-means algorithm to obtain a cluster partitioning of the image
Saint, Michel Thierry. "Filtrage non linéaire en vue d'une segmentation semi supervisée appliquée à l'imagerie médicale". Lille 1, 1997. http://www.theses.fr/1997LIL10110.
Pełny tekst źródłaMignotte, Max. "Segmentation d'images sonar par approche markovienne hiérarchique non supervisée et classification d'ombres portées par modèles statistiques". Brest, 1998. http://www.theses.fr/1998BRES2017.
Pełny tekst źródłaQuelle, Hans-Christoph. "Segmentation bayesienne non supervisee en imagerie radar". Rennes 1, 1993. http://www.theses.fr/1993REN10012.
Pełny tekst źródłaMartel-Brisson, Nicolas. "Approche non supervisée de segmentation de bas niveau dans un cadre de surveillance vidéo d'environnements non contrôlés". Thesis, Université Laval, 2012. http://www.theses.ulaval.ca/2012/29093/29093.pdf.
Pełny tekst źródłaGiordana, Nathalie. "Segmentation non supervisee d'images multi-spectrales par chaines de markov cachees". Compiègne, 1996. http://www.theses.fr/1996COMP981S.
Pełny tekst źródłaCzęści książek na temat "Segmentation non supervisée d'images"
ATTO, Abdourrahmane M., Fatima KARBOU, Sophie GIFFARD-ROISIN i Lionel BOMBRUN. "Clustering fonctionnel de séries d’images par entropies relatives". W Détection de changements et analyse des séries temporelles d’images 1, 121–38. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch4.
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