The Gaussian random field Y ( t ), t ∈ T , is one of the most common models used to describe spatial stochastic processes. In many applications, the domain T is a subset of D -dimensional Euclidean space (usually D = 2 or D = 3), and the function Y ( t ) is almost surely ...
Objective To propose an improved C-means segment method based on Gibbs random field accelerated by GPU. 结论:采用显示卡加速的基于Gibbs随机场的模糊C均值分割算法运算接近实时,大大提高了Gibbs随机场分割算法在临床的实用性。 3. In order to overcome the limitation of Gauss mixture model(GMM),this art...
i.e.,\(F = - k_{B} T\,\ln Z\). These factors do not play a role in determining the approximation of the partition function. They are also irrelevant ifZrepresents the partition function of a spatial random field instead of a system of particles at thermal equilibrium...
The Gaussian random field method considers a stochastic random field in the three-dimensional space, which is constrained to have a number of statistical properties corresponding to the physical material. From: Studies in Surface Science and Catalysis, 2002 ...
Exact moduli of continuity for operator-scaling Gaussian random fields Let $X=\\{X(t),t\\in\\mathrm{R}^N\\}$ be a centered real-valued operator-scaling Gaussian random field with stationary increments, introduced by Bierm\\'{e... Y Li,W Wang,Y Xiao - 《Bernoulli》 被引量: 27发表...
The selection Gaussian random field can capture skewness, multi-modality, and to some extend heavy tails in the marginal distribution. We present a Metropolis-Hastings algorithm for efficient simulation of realizations from the random field, and a numerical algorithm for estimating model parameters by ...
To simulate different layers in the subsurface, the permeability is modelled as a piecewise constant or piecewise spatially correlated random field, including the possibility of piecewise log-normal random fields. The location of the layers ... M Park,A Teckentrup - 《Mathematics》 被引量: 5发表...
GaussianMarkovRandomFieldModels Introduction Why? Why? Gaussians(andmostoftenintheformofaGMRF)are extensivelyusedinstatisticalmodels. However,itsexcellentcomputationalpropertiesarenotoften explored. Lackofknowledgeofgeneralpurposeandnearoptimal numericalalgorithms GaussianMarkovRandomFieldModels Introduction Why? Why?
Niranjan, "Gaussian Markov random field based improved texture descriptor for image segmentation", Image and Vision Computing, vol. 32, no. 11, pp. 884-895, 2014.Dharmagunawardhana.C,Mahmoodi. S, Bennett. M, et al. Gaussian Markov random field based improved texture descriptor for image ...
The skewness in the skew-Gaussian random field is found to be strongly influenced by the spatial coupling in the field, and the parameter estimators appear as consistent with increasing size of the random field. Moreover, we use the closed skew-normal distribution in a multivariate random field...