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

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dc.contributor.author Nicolis, Orietta
dc.contributor.author Garutti, Claudio
dc.contributor.author Vidakovic, Brani
dc.date.accessioned 2008-11-26T16:57:34Z
dc.date.available 2008-11-26T16:57:34Z
dc.date.issued 2006
dc.identifier.uri http://hdl.handle.net/1853/25838
dc.description.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 en
dc.language.iso en_US en
dc.publisher Georgia Institute of Technology en
dc.relation.ispartofseries Biomedical Engineering Technical Report ; 02/2006 en
dc.subject Scaling en
dc.subject Wavelets en
dc.subject Self-similarity en
dc.subject 2D wavelet spectra en
dc.title 2-D Wavelet-Based Spectra with Application in Analysis of Geophysical Images en
dc.type Technical Report en
dc.contributor.corporatename Georgia Institute of Technology. Dept. of Biomedical Engineering
dc.contributor.corporatename Emory University. Dept. of Biomedical Engineering
dc.contributor.corporatename Università di Bergamo. Dipartimento di Ingegneria Informatica e Metodi Quantitativi
dc.contributor.corporatename Università di Padova.Dipartimento di ingegneria dell'informazione


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