Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models and Image Processing
Bayesian Approach to Image Interpretation
Bayesian Approach to Image Interpretation
Universal Analytical Forms for Modeling Image Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet transforms on two-dimensional images
Mathematical and Computer Modelling: An International Journal
Self-similar texture modeling using FARIMA processes with applications to satellite images
IEEE Transactions on Image Processing
Machine Graphics & Vision International Journal
Clustering stability-based feature selection for unsupervised texture classification
Machine Graphics & Vision International Journal
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We present a phenomenological parametric model for the spectrum of the discrete Fourier transform (DFT) of the images obtained from the Extreme UV Imaging Telescope (EIT) of the SOHO mission. As this spectrum decays very fast, we model its logarithm rather than the original spectrum. The proposed model is rotation-invariant. The vicinity of the direct current (DC) component of the logarithm of the spectrum is modelled by the sum of two exponential functions, while the region of high frequencies is modelled by a single exponential function summed with a constant. We discuss a method for fitting the model to the experimental data, show the results of numerical experiments, and discuss various measures of goodness of the fit. The fitting of the described model was carried out for a sequence of images covering one year, and the time evolution of the measures of goodness of fit is also presented.