Literatura científica selecionada sobre o tema "Traitement d'images – Techniques numériques – Segmentation bayésienne"
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Teses / dissertações sobre o assunto "Traitement d'images – Techniques numériques – Segmentation bayésienne"
Benboudjema, Dalila. "Champs de Markov triplets et segmentation bayésienne non supervisée d'images". Evry, Institut national des télécommunications, 2005. http://www.theses.fr/2005TELE0009.
Texto completo da fonteImage 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
Quelle, Hans-Christoph. "Segmentation bayesienne non supervisee en imagerie radar". Rennes 1, 1993. http://www.theses.fr/1993REN10012.
Texto completo da fonteMignotte, 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.
Texto completo da fonteBricq, Stéphanie. "Segmentation d’images IRM anatomiques par inférence bayésienne multimodale et détection de lésions". Université Louis Pasteur (Strasbourg) (1971-2008), 2008. https://publication-theses.unistra.fr/public/theses_doctorat/2008/BRICQ_Stephanie_2008.pdf.
Texto completo da fonteMedical imaging provides a growing number of data. Automatic segmentation has become a fundamental step for quantitative analysis of these images in many brain diseases such as multiple sclerosis (MS). We focused our study on brain MRI segmentation and MS lesion detection. At first we proposed a method of brain tissue segmentation based on hidden Markov chains taking into account neighbourhood information. This method can also include prior information provided by a probabilistic atlas and takes into account the artefacts appearing on MR images. Then we extended this method to detect MS lesions thanks to a robust estimator and prior information provided by a probabilistic atlas. We have also developed a 3D MRI segmentation method based on statistical active contours to refine the lesion segmentation. The results were compared with other existing methods of segmentation, and with manual expert segmentations
Chapoulie, Alexandre. "Contributions aux méthodes de détection visuelle de fermeture de boucle et de segmentation topologique de l'environnement". Nice, 2012. http://www.theses.fr/2012NICE4055.
Texto completo da fonteIn the context of global localization and, more widely, in Simultaneous Localization and Mapping, it is mandatory to be able to detect if a robust comes to a previously visited place. It is the loop closure detection problem. Algorithms, in visual place recognition, usually allow detection in real-time, are robust to perceptual aliasing or even to dynamic objects. Those algorithms are often sensitive to the robot orientation involving an impossibility to detect a loop closure from a different point of view. In order to alleviate this drawback, panoramic or omnidirectional cameras are often used. We propose a more general representation of the environment with an ego-centric spherical view. Using these representation properties, we elaborate a loop closure detection algorithm that satisfies, in addition to other properties, robot orientation independence. The environment model is often a set of images taken at various moments, each image corresponding to a place. Existing methods cluster those images ion meaning places of the environment, the topological places, using the concept of covisibility of information between places. Our approach relies on the utilization of the environment structure. We hence define a topological place as having a structure which does not change, variation leading to a place change. The structure variations are detected with an efficient change-point detection algorithm
Rousson, Mikaël. "Cue integration and front evolution in image segmentation". Nice, 2004. http://www.theses.fr/2004NICE4100.
Texto completo da fonteAutomatic detection and selection of regions of interest is a key step in image understanding. In the literature, most segmentation approaches are restricted to a particular class of images. This limitation is due to the large variety of cues available to characterize a region of interest. Targeting particular applications, algorithms are centered on the from most relevant cue. The limiting factor to obtain a general algorithm is the large variety of cues available to characterize a region of interest. It can be gray-level, color, texture, shape, etc. . . In this thesis, we propose a general formulation able to deal with each one of these characteristics. Image intensity, color, texture, motion and prior shape knowledge are considered. For this purpose, a probabilistic inference is obtained from a Bayesian formulation of the segmentation problem. Then, reformulated as an energy minimization, the most probable image partition is obtained using front evolution techniques. Level-set functions are naturally introduced to represent the evolving fronts while region statistics are optimized in parallel. This framework can naturally handle scalar and vector-valued smooth images but more complex cues are also integrated. Texture and motion features, as well as prior shape knowledge are successively introduced. Complex medical images are considered in the last part with the case of diffusion magnetic resonance images which gives 3D probability density fields
Scherrer, Benoit. "Segmentation des tissus et structures sur les IRM cérébrales : agents markoviens locaux et coopératifs et formulation bayésienne". Grenoble INPG, 2008. https://tel.archives-ouvertes.fr/tel-00361317.
Texto completo da fonteAccurate magnetic resonance brain scan segmentation is critical in a number of clinical and neuroscience applications. This task is challenging due to artifacts, low contrast between tissues and inter-individual variability that inhibit the introduction of a priori knowledge. In this thesis, we propose a new MR brain scan segmentation approach. Unique features of this approach include (1) the coupling of tissue segmentation, structure segmentation and prior knowledge construction, and (2) the consideration of local image properties. Locality is modeled through a multi-agent framework: agents are distributed into the volume and perform a local Markovian segmentation. As an initial approach (LOCUS, Local Cooperative Unified Segmentation), intuitive cooperation and coupling mechanisms are proposed to ensure the consistency of local models. Structures are segmented via the introduction of spatial localization constraints based on fuzzy spatial relations between structures. In a second approach, (LOCUS-B, LOCUS in a Bayesian framework) we consider the introduction of a statistical atlas to describe structures. The problem is reformulated in a Bayesian framework, allowing a statistical formalization of coupling and cooperation. Tissue segmentation, local model regularization, structure segmentation and local affine atlas registration are then coupled in an EM framework and mutually improve. The evaluation on simulated and real images shows good results, and in particular, a robustness to non-uniformity and noise with low computational cost. Local distributed and cooperative MRF models then appear as a powerful and promising approach for medical image segmentation
Pons, Isabelle. "Méthodes de segmentation bayésienne appliquées aux images SAR : théorie et mise en oeuvre". Nice, 1994. http://www.theses.fr/1994NICE4714.
Texto completo da fonteGarcía, Lorenzo Daniel. "Robust segmentation of focal lesions on multi-sequence MRI in multiple sclerosis". Rennes 1, 2010. http://www.theses.fr/2010REN1S018.
Texto completo da fonteMultiple sclerosis (MS) affects around 80. 000 people in France. Magnetic resonance imaging (MRI) is an essential tool for diagnosis of MS and MRI-derived surrogate markers such as MS lesion volumes are often used as measures in MS clinical trials for the development of new treatments. The manual segmentation of these MS lesions is a time-consuming task that shows high inter- and intra-rater variability. We developed an automatic workflow for the segmentation of focal MS lesions on MRI. The segmentation method is based on the robust estimation of a parametric model of the intensities of the brain; lesions are detected as outliers to the model. We proposed two methods to include spatial information in the segmentation using mean shift and graph cut. We performed a quantitative evaluation of our workflow using synthetic and clinical images of two different centers to verify its accuracy and robustness
Garcia, Arnaud. "Analyse statistique et morphologique des images multivaluées : développements logiciels pour les applications cliniques". Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2008. http://tel.archives-ouvertes.fr/tel-00422589.
Texto completo da fonteLivros sobre o assunto "Traitement d'images – Techniques numériques – Segmentation bayésienne"
Computational analysis of visual motion. New York: Plenum Press, 1994.
Encontre o texto completo da fonteDigital image processing. 6a ed. Berlin: Springer, 2005.
Encontre o texto completo da fonteDigital image processing: Concepts, algorithms, and scientific applications. 4a ed. Berlin: Springer, 1997.
Encontre o texto completo da fonteJähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 3a ed. Berlin: Springer-Verlag, 1995.
Encontre o texto completo da fonteJähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. 2a ed. Berlin: Springer-Verlag, 1993.
Encontre o texto completo da fonteJähne, Bernd. Digital image processing: Concepts, algorithms, and scientific applications. Berlin: Springer-Verlag, 1991.
Encontre o texto completo da fonteInsight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. AK Peters, 2004.
Encontre o texto completo da fonteMitiche, Amar. Computational Analysis of Visual Motion. Springer, 2013.
Encontre o texto completo da fonte