P. P'erez and F. Heitz, "Global Bayesian estimation, constrained multiscale Markov random fields and the analysis of visual motion, " In A. Mohammad-Djafari and G. Demoments, editors, Maximum Entropy and Bayesian Methods, pp. 383-388, Academic Publishers, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... models have been successfully introduced in many fundamental issues of image analysis and computer vision such as image restoration [5] 14] edge detection [13] image segmentation [9] 13] computed tomography [11] surface reconstruction [9] 30] stereovision [2] motion analysis [18] 24] [37], or scene interpretation [33] The mathematical framework is a statistical one: entities of interest in a given task are described by statistical models (Markov random fields) and Bayesian estimation theory is used to extract the relevant information from the observed images. By defining ....

....of MRF s. The general properties of several subsampling schemes are examined. It is shown that most standard subsampling schemes lead Alternate approaches, involving various kind of coarsening operators on a single resolution model, have also been developed recently [7] 19] 20] 27] 34] 36] [37]. These approaches are not subject to a loss of locality and hence will not be considered in this paper. to a loss of locality. Examples of subsampling schemes which preserve a local Markov property are also presented. The statistical properties of MRF models defined on trees [3] 6] 28] or ....

P. P'erez and F. Heitz, "Global Bayesian estimation, constrained multiscale Markov random fields and the analysis of visual motion, " In A. Mohammad-Djafari and G. Demoments, editors, Maximum Entropy and Bayesian Methods, pp. 383-388, Academic Publishers, 1993.

.... models have been successfully introduced in many fundamental issues of image analysis and computer vision such as image restoration [5] 14] edge detection [13] image segmentation [9] 13] computed tomography [11] surface reconstruction [9] 30] stereovision [2] motion analysis [18] 24] [37], or scene interpretation [33] The mathematical framework is a statistical one: entities of interest in a given task are described by statistical models (Markov random fields) and Bayesian estimation theory is used to extract the relevant information from the observed images. By defining ....

....MRF s. The general properties of several subsampling schemes are examined. It is shown that most standard subsampling schemes lead 1 Alternate approaches, involving various kind of coarsening operators on a single resolution model, have also been developed recently [7] 19] 20] 27] 34] 36] [37]. These approaches are not subject to a loss of locality and hence will not be considered in this paper. to a loss of locality. Examples of subsampling schemes which preserve a local Markov property are also presented. The statistical properties of MRF models defined on trees [3] 6] 28] or ....

P. P'erez and F. Heitz, "Global Bayesian estimation, constrained multiscale Markov random fields and the analysis of visual motion, " In A. Mohammad-Djafari and G. Demoments, editors, Maximum Entropy and Bayesian Methods, pp. 383-388, Academic Publishers, 1993.

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