Self-similar texture characterization using a Fourier-domain maximum likelihood estimation method
Pattern Recognition Letters
On least squares estimation for long-memory lattice processes
Journal of Multivariate Analysis
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Multifractal signature estimation for textured image segmentation
Pattern Recognition Letters
Locally constrained synthetic LoDs generation for natural terrain meshes
Future Generation Computer Systems
Empirical mode decomposition synthesis of fractional processes in 1D- and 2D-space
Image and Vision Computing
Estimation of heart rate signals for mental stress assessment using neuro fuzzy technique
Applied Soft Computing
Estimating the Long-Memory Parameter in Nonstationary Processes Using Wavelets
Computational Economics
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The role of the wavelet transformation as a whitening filter for 1/f processes is exploited to address problems of parameter and signal estimations for 1/f processes embedded in white background noise. Robust, computationally efficient, and consistent iterative parameter estimation algorithms are derived based on the method of maximum likelihood, and Cramer-Rao bounds are obtained. Included among these algorithms are optimal fractal dimension estimators for noisy data. Algorithms for obtaining Bayesian minimum-mean-square signal estimates are also derived together with an explicit formula for the resulting error. These smoothing algorithms find application in signal enhancement and restoration. The parameter estimation algorithms find application in signal enhancement and restoration. The parameter estimation algorithms, in addition to solving the spectrum estimation problem and to providing parameters for the smoothing process, are useful in problems of signal detection and classification. Results from simulations are presented to demonstrated the viability of the algorithms