2-D Wavelet-Based Spectra with Application in Analysis of Geophysical Images

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/25838

Title: 2-D Wavelet-Based Spectra with Application in Analysis of Geophysical Images
Author: Nicolis, Orietta ; Garutti, Claudio ; Vidakovic, Brani
Abstract: We propose a wavelet-based spectral method for estimating the (directional) Hurst parameter in isotropic and anisotropic non-stationary fractional Gaussian fields. The method can be applied to self-similar images and, in general, to d- dimensional data that scale. In the application part, we consider denoising of 2-D fractional Brownian fields and the classification of the clouds/temperature satellite images. In the first application, we use Bayesian inference calibrated by information from the wavelet-spectral domain to separate the signal, in this case the 2-D Brownian field, and the noise. For the classification of geophysical images we first estimate directional Hurst exponents and use them as an input to standard machine learning algorithms
Type: Technical Report
URI: http://hdl.handle.net/1853/25838
Date: 2006
Contributor: Georgia Institute of Technology. Dept. of Biomedical Engineering
Emory University. Dept. of Biomedical Engineering
Università di Bergamo. Dipartimento di Ingegneria Informatica e Metodi Quantitativi
Università di Padova.Dipartimento di ingegneria dell'informazione
Relation: Biomedical Engineering Technical Report ; 02/2006
Publisher: Georgia Institute of Technology
Subject: Scaling
Wavelets
Self-similarity
2D wavelet spectra

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