Hazard rate estimation on random fields

Estimation of Random Fields

Let u(x) = s(x) + n(x) , where s(x) and n(x) are random fields, s(x) is useful signal, n(x) is noise. Assume that s(x) = n(x) = 0 , where the bar stands for mean value. Suppose that the covariance functions R(x, y) := u * (x)u(y) and f (x, y) := u * (x)s(y) are known, where the asterisk stands for complex conjugate. Assume that u(x) is observed in a bounded domain D ⊂ R r of the Euclidean space...

Contour Estimation on Piecewise Homogeneous Random Fields

Contour Estimation, Bayesian Estimation, Random Fields, Dynamic Programming, Multigrid Methods. This paper addresses contour estimation on images modeled as piecewise homogeneous random elds. It is therefore assumed that images are samples of random elds composed of a set of homogeneous, in a statistical sense, regions; pixels within each region are assumed to be independent samples of a given ...

On a Parameter Estimation Method for Gibbs-Markov Random Fields

Fig. 2. space by Patrick-Fisher's algorithm (solid line) and E (dotted line). Bayes error estimates for SONAR data transformed to IO-dimensional high-dimensional data these results might be more in favor of E.) This is a result of the fact that each iteration of simplex requires that the samples be transformed to the low-dimensional space, and then the Bayes error estimated in that space, which...

Efficient Parallel Estimation for Markov Random Fields

We present a new , deterministic, distributed MAPes­ timation algorithm for Markov Random Fields called Local Highest Confidence First (Local HCF). The al­ gorithm has been applied to segmentation problems in computer vision and its performance compared with stochastic algorithms. The experiments show that Local HCF finds better estimates than stochas­ tic algorithms with much less computation.

Consistent Change Estimation in 2D Random Fields