Decomposition of Surface Data into Fractal Signals Based on Mean Likelihood and Importance Sampling and Its Applications to Feature Extraction

Shozo TOKINAGA; Noboru TAKAGI

doi:10.1093/ietfec/e88-a.7.1946

Decomposition of Surface Data into Fractal Signals Based on Mean Likelihood and Importance Sampling and Its Applications to Feature Extraction

Shozo TOKINAGA, Noboru TAKAGI

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This paper deals with the decomposition of surface data into several fractal signal based on the parameter estimation by the Mean Likelihood and Importance Sampling (IS) based on the Monte Carlo simulations. The method is applied to the feature extraction of surface data. Assuming the stochastic models for generating the surface, the likelihood function is defined by using wavelet coefficients and the parameter are estimated based on the mean likelihood by using the IS. The approximation of the wavelet coefficients is used for estimation as well as the statistics defined for the variances of wavelet coefficients, and the likelihood function is modified by the approximation. After completing the decomposition of underlying surface data into several fractal surface, the prediction method for the fractal signal is employed based on the scale expansion based on the self-similarity of fractal geometry. After discussing the effect of additive noise, the method is applied to the feature extraction of real distribution of surface data such as the cloud and earthquakes.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.7 pp.1946-1956

Publication Date: 2005/07/01

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DOI: 10.1093/ietfec/e88-a.7.1946

Type of Manuscript: PAPER

Category: Digital Signal Processing

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Shozo TOKINAGA, Noboru TAKAGI, "Decomposition of Surface Data into Fractal Signals Based on Mean Likelihood and Importance Sampling and Its Applications to Feature Extraction" in IEICE TRANSACTIONS on Fundamentals, vol. E88-A, no. 7, pp. 1946-1956, July 2005, doi: 10.1093/ietfec/e88-a.7.1946.
Abstract: This paper deals with the decomposition of surface data into several fractal signal based on the parameter estimation by the Mean Likelihood and Importance Sampling (IS) based on the Monte Carlo simulations. The method is applied to the feature extraction of surface data. Assuming the stochastic models for generating the surface, the likelihood function is defined by using wavelet coefficients and the parameter are estimated based on the mean likelihood by using the IS. The approximation of the wavelet coefficients is used for estimation as well as the statistics defined for the variances of wavelet coefficients, and the likelihood function is modified by the approximation. After completing the decomposition of underlying surface data into several fractal surface, the prediction method for the fractal signal is employed based on the scale expansion based on the self-similarity of fractal geometry. After discussing the effect of additive noise, the method is applied to the feature extraction of real distribution of surface data such as the cloud and earthquakes.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.7.1946/_p

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@ARTICLE{e88-a_7_1946,
author={Shozo TOKINAGA, Noboru TAKAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Decomposition of Surface Data into Fractal Signals Based on Mean Likelihood and Importance Sampling and Its Applications to Feature Extraction},
year={2005},
volume={E88-A},
number={7},
pages={1946-1956},
abstract={This paper deals with the decomposition of surface data into several fractal signal based on the parameter estimation by the Mean Likelihood and Importance Sampling (IS) based on the Monte Carlo simulations. The method is applied to the feature extraction of surface data. Assuming the stochastic models for generating the surface, the likelihood function is defined by using wavelet coefficients and the parameter are estimated based on the mean likelihood by using the IS. The approximation of the wavelet coefficients is used for estimation as well as the statistics defined for the variances of wavelet coefficients, and the likelihood function is modified by the approximation. After completing the decomposition of underlying surface data into several fractal surface, the prediction method for the fractal signal is employed based on the scale expansion based on the self-similarity of fractal geometry. After discussing the effect of additive noise, the method is applied to the feature extraction of real distribution of surface data such as the cloud and earthquakes.},
keywords={},
doi={10.1093/ietfec/e88-a.7.1946},
ISSN={},
month={July},}

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TY - JOUR
TI - Decomposition of Surface Data into Fractal Signals Based on Mean Likelihood and Importance Sampling and Its Applications to Feature Extraction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1946
EP - 1956
AU - Shozo TOKINAGA
AU - Noboru TAKAGI
PY - 2005
DO - 10.1093/ietfec/e88-a.7.1946
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2005
AB - This paper deals with the decomposition of surface data into several fractal signal based on the parameter estimation by the Mean Likelihood and Importance Sampling (IS) based on the Monte Carlo simulations. The method is applied to the feature extraction of surface data. Assuming the stochastic models for generating the surface, the likelihood function is defined by using wavelet coefficients and the parameter are estimated based on the mean likelihood by using the IS. The approximation of the wavelet coefficients is used for estimation as well as the statistics defined for the variances of wavelet coefficients, and the likelihood function is modified by the approximation. After completing the decomposition of underlying surface data into several fractal surface, the prediction method for the fractal signal is employed based on the scale expansion based on the self-similarity of fractal geometry. After discussing the effect of additive noise, the method is applied to the feature extraction of real distribution of surface data such as the cloud and earthquakes.
ER -