Title :
Filtered fractals in signal modeling
Author :
Deriche, M. ; Tewfik, Ahmed H.
Author_Institution :
Signal Processing Res. Center, Queensland Univ. of Technol., Brisbane, Australia
Abstract :
Filtered versions of fractionally differenced Gaussian noise (FDGN) processes are studied. Fractionally differenced Gaussian noise is a discrete-time equivalent of fractional Brownian motion. Filtered versions of such processes are ideally suited for modeling signals with both short-term and long-term correlation structures. Two iterative algorithms for estimating the parameters of filtered FDGN processes are described
Keywords :
Gaussian noise; correlation theory; filtering theory; fractals; iterative methods; correlation structures; discrete-time equivalent; filtered FDGN processes; fractional Brownian motion; fractionally differenced Gaussian noise; iterative algorithms; signal modeling; 1f noise; Autoregressive processes; Computer vision; Filters; Footwear; Fractals; Image processing; Maximum likelihood estimation; Parameter estimation; Signal processing;
Conference_Titel :
Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-1281-3
DOI :
10.1109/ISCAS.1993.393772
