Open AccessMODELS OF COVARIANCE FUNCTIONS OF GAUSSIAN RANDOM FIELDS ESCAPING FROM ISOTROPY, STATIONARITY AND NON NEGATIVITY
Author(s) -
Pablo Gregori,
Emilio Porcu,
Jorge Mateu
Publication year - 2014
Publication title -
image analysis and stereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.237
H-Index - 27
eISSN - 1854-5165
pISSN - 1580-3139
DOI - 10.5566/ias.v33.p75-81
Subject(s) - isotropy , covariance , gaussian , random field , mathematics , covariance function , basis (linear algebra) , geostatistics , statistical physics , space (punctuation) , fourier transform , mathematical analysis , statistics , computer science , physics , geometry , spatial variability , quantum mechanics , operating system
This paper represents a survey of recent advances in modeling of space or space-time Gaussian Random Fields (GRF), tools of Geostatistics at hand for the understanding of special cases of noise in image analysis. They can be used when stationarity or isotropy are unrealistic assumptions, or even when negative covariance between some couples of locations are evident. We show some strategies in order to escape from these restrictions, on the basis of rich classes of well known stationary or isotropic non negative covariance models, and through suitable operations, like linear combinations, generalized means, or with particular Fourier transforms