Markov Random Field
Mostrando 1-5 de 5 artigos, teses e dissertações.
-
1. REFINAMENTO GEOMÉTRICO DE CONTORNOS E CUMEEIRAS DE TELHADOS DE EDIFÍCIOS EXTRAÍDOS DE DADOS LASER COM USO DE IMAGEM AÉREA
Nesse artigo é proposto um método para refinar geometricamente telhados 3D extraídos dos dados LASER com o uso de uma imagem aérea de alta-resolução e modelos de campo aleatório de Markov (MRF - Markov Random Field).Para tanto, uma descrição MRF para agrupamento de retas é desenvolvida, assumindo que cada lado de contorno e cumeeira projetado está
Bol. Ciênc. Geod.. Publicado em: 2014-09
-
2. 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
-
3. "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
-
4. 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.
-
5. Bayesian image reconstruction for emission tomography incorporating Good's roughness prior on massively parallel processors.
Since the introduction by Shepp and Vardi [Shepp, L. A. & Vardi, Y. (1982) IEEE Trans. Med. Imaging 1, 113-121] of the expectation-maximization algorithm for the generation of maximum-likelihood images in emission tomography, a number of investigators have applied the maximum-likelihood method to imaging problems. Though this approach is promising, it is now