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dc.contributor.authorNicolis, Orietta
dc.contributor.authorGarutti, Claudio
dc.contributor.authorVidakovic, Brani
dc.date.accessioned2008-11-26T16:57:34Z
dc.date.available2008-11-26T16:57:34Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/1853/25838
dc.description.abstractWe 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 algorithmsen
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesBiomedical Engineering Technical Report ; 02/2006en
dc.subjectScalingen
dc.subjectWaveletsen
dc.subjectSelf-similarityen
dc.subject2D wavelet spectraen
dc.title2-D Wavelet-Based Spectra with Application in Analysis of Geophysical Imagesen
dc.typeTechnical Reporten
dc.contributor.corporatenameGeorgia Institute of Technology. Dept. of Biomedical Engineering
dc.contributor.corporatenameEmory University. Dept. of Biomedical Engineering
dc.contributor.corporatenameUniversità di Bergamo. Dipartimento di Ingegneria Informatica e Metodi Quantitativi
dc.contributor.corporatenameUniversità di Padova.Dipartimento di ingegneria dell'informazione


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