Markov Random Fields
Mostrando 1-7 de 7 artigos, teses e dissertações.
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1. Graphical models and point pattern matching
This paper describes a novel solution to the rigid point pattern matching problem in Euclidean spaces of any dimension. Although we assume rigid motion, jitter is allowed. We present a noniterative, polynomial time algorithm that is guaranteed to find an optimal solution for the noiseless case. First, we model point pattern matching as a weighted graph match
Publicado em: 2011
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2. Avaliação de descritores de textura para segmentação não-supervisionada de imagens / Texture descriptors evalution for unsupervised image segmentation
Este trabalho consiste em uma avaliação de descritores de atributos de textura para o caso totalmente não-supervisionado, na qual nada se conhece anteriormente sobre a natureza das texturas ou o número de regiões presentes na imagem. Escolheram-se para descrever as texturas decomposição por filtros de Gabor, descritores escalares baseados em matrizes
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 19/03/2010
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3. Combinação de modelos de campos aleatórios markovianos para classificação contextual de imagens multiespectrais / Combining markov random field models for multispectral image contextual classification
This work presents a novel MAP-MRF approach for multispectral image contextual classification by combining higher-order Markov Random Field models. The statistical modeling follows the Bayesian paradigm, with the definition of a multispectral Gaussian Markov Random Field model for the observations and a Potts MRF model to represent the a priori knowledge. In
Publicado em: 2010
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4. MÃtodo adaptativo de Markov Chain Monte Carlo para manipulaÃÃo de modelos Bayesianos
Historically, Bayesian models have deserved special attention from academy and applied fields mainly by allowing mathematical combination of human judgments and empirical data. Markov Chain Monte Carlo (MCMC) methodology is one of the main classes of approaches for computing marginal estimates from Bayesian models. Among Markov Chain Monte Carlo methods, Met
Publicado em: 2009
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5. Transition models for binary data / Modelos de transição para dados binários
Binary or dichotomous data are quite common in many fields of Sciences in which there is an interest in registering the occurrence of a particular event. On the other hand, when each sampled unit is evaluated in more than one occasion, we have longitudinal data or repeated measures over time. It is also common, in longitudinal studies, to have explanatory va
Publicado em: 2007
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6. "Segmentação de imagens e validação de classes por abordagem estocástica" / Image segmentation and class validation in a stochastic approach
An important stage of the automatic image analysis process is segmentation, that aims to split an image into regions whose pixels exhibit a certain degree of similarity. Texture is known as an efficient feature that provides enough discriminant power to differenciate pixels from distinct regions. It is usually defined as a random combination of pixel intensi
Publicado em: 2006
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7. Mixed Markov models
Markov random fields can encode complex probabilistic relationships involving multiple variables and admit efficient procedures for probabilistic inference. However, from a knowledge engineering point of view, these models suffer from a serious limitation. The graph of a Markov field must connect all pairs of variables that are conditionally dependent ev
National Academy of Sciences.