Estimation of Noncausal Model for Random Image with Double Peak Spectrum

Shigeyuki MIYAGl; Hisanao OGURA

Estimation of Noncausal Model for Random Image with Double Peak Spectrum

Shigeyuki MIYAGl, Hisanao OGURA

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A new type of noncausal stochastic model is proposed to represent a random image with double peak spectrum. The model based on the assumption that the double peak spectrum is expressed by a product of two spectra located at two symmetric positions in the 2D spatial frequency space. Estimation of model parameters is made by means of minimizing the "whiteness" which was proposed in authors' previous work. In a simulation for model estimation we make use of computer-generated random images with double peak spectrum. Comparing this with the estimation by a causal model, we demonstrate that the present method can better estimate not only the spectral peak location but also the spectral shape. The proposed model can be extend to an image model with multl-peak spectrum. However, Increase of parameters makes the model estimation more difficult We try a model with triple peak spectra since a real texture image usually possesses a spectral peak at the origin besides the two peaks. A result shows that the estimation of three spectral positions are good enough, but their spectral shapes are not necessarily satisfactory. It is expected that the estimation of multi-peaked spectral model can be made better by improving the process of minimizing the "whiteness."

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.10 pp.1725-1732

Publication Date: 1996/10/25

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Type of Manuscript: PAPER

Category: Image Theory

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Shigeyuki MIYAGl, Hisanao OGURA, "Estimation of Noncausal Model for Random Image with Double Peak Spectrum" in IEICE TRANSACTIONS on Fundamentals, vol. E79-A, no. 10, pp. 1725-1732, October 1996, doi: .
Abstract: A new type of noncausal stochastic model is proposed to represent a random image with double peak spectrum. The model based on the assumption that the double peak spectrum is expressed by a product of two spectra located at two symmetric positions in the 2D spatial frequency space. Estimation of model parameters is made by means of minimizing the "whiteness" which was proposed in authors' previous work. In a simulation for model estimation we make use of computer-generated random images with double peak spectrum. Comparing this with the estimation by a causal model, we demonstrate that the present method can better estimate not only the spectral peak location but also the spectral shape. The proposed model can be extend to an image model with multl-peak spectrum. However, Increase of parameters makes the model estimation more difficult We try a model with triple peak spectra since a real texture image usually possesses a spectral peak at the origin besides the two peaks. A result shows that the estimation of three spectral positions are good enough, but their spectral shapes are not necessarily satisfactory. It is expected that the estimation of multi-peaked spectral model can be made better by improving the process of minimizing the "whiteness."
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_10_1725/_p

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@ARTICLE{e79-a_10_1725,
author={Shigeyuki MIYAGl, Hisanao OGURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Estimation of Noncausal Model for Random Image with Double Peak Spectrum},
year={1996},
volume={E79-A},
number={10},
pages={1725-1732},
abstract={A new type of noncausal stochastic model is proposed to represent a random image with double peak spectrum. The model based on the assumption that the double peak spectrum is expressed by a product of two spectra located at two symmetric positions in the 2D spatial frequency space. Estimation of model parameters is made by means of minimizing the "whiteness" which was proposed in authors' previous work. In a simulation for model estimation we make use of computer-generated random images with double peak spectrum. Comparing this with the estimation by a causal model, we demonstrate that the present method can better estimate not only the spectral peak location but also the spectral shape. The proposed model can be extend to an image model with multl-peak spectrum. However, Increase of parameters makes the model estimation more difficult We try a model with triple peak spectra since a real texture image usually possesses a spectral peak at the origin besides the two peaks. A result shows that the estimation of three spectral positions are good enough, but their spectral shapes are not necessarily satisfactory. It is expected that the estimation of multi-peaked spectral model can be made better by improving the process of minimizing the "whiteness."},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - Estimation of Noncausal Model for Random Image with Double Peak Spectrum
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1725
EP - 1732
AU - Shigeyuki MIYAGl
AU - Hisanao OGURA
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1996
AB - A new type of noncausal stochastic model is proposed to represent a random image with double peak spectrum. The model based on the assumption that the double peak spectrum is expressed by a product of two spectra located at two symmetric positions in the 2D spatial frequency space. Estimation of model parameters is made by means of minimizing the "whiteness" which was proposed in authors' previous work. In a simulation for model estimation we make use of computer-generated random images with double peak spectrum. Comparing this with the estimation by a causal model, we demonstrate that the present method can better estimate not only the spectral peak location but also the spectral shape. The proposed model can be extend to an image model with multl-peak spectrum. However, Increase of parameters makes the model estimation more difficult We try a model with triple peak spectra since a real texture image usually possesses a spectral peak at the origin besides the two peaks. A result shows that the estimation of three spectral positions are good enough, but their spectral shapes are not necessarily satisfactory. It is expected that the estimation of multi-peaked spectral model can be made better by improving the process of minimizing the "whiteness."
ER -