Signal processing with fractals: a wavelet-based approach
Signal processing with fractals: a wavelet-based approach
Local Whittle estimator for anisotropic random fields
Journal of Multivariate Analysis
A wavelet-based joint estimator of the parameters of long-range dependence
IEEE Transactions on Information Theory
Wavelet packet decompositions for the analysis of 2-D fields with stationary fractional increments
IEEE Transactions on Information Theory
Correlation structure of the discrete wavelet coefficients of fractional Brownian motion
IEEE Transactions on Information Theory - Part 2
Wavelet analysis and synthesis of fractional Brownian motion
IEEE Transactions on Information Theory - Part 2
Spectral representation of fractional Brownian motion in n dimensions and its properties
IEEE Transactions on Information Theory
A 2D wavelet-based multiscale approach with applications to the analysis of digital mammograms
Computational Statistics & Data Analysis
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A wavelet-based spectral method for estimating the (directional) Hurst parameter in isotropic and anisotropic non-stationary fractional Gaussian fields is proposed. The method can be applied to self-similar images and, in general, to d-dimensional data which scale. In the application part, the problems of denoising 2D fractional Brownian fields and classification of digital mammograms to benign and malignant are considered. In the first application, a Bayesian inference calibrated by information from the wavelet-spectral domain is used to separate the signal from the noise. In the second application, digital mammograms are classified into benign and malignant based on the directional Hurst exponents which prove to be discriminatory summaries.