Rotation and Scaling Invariant Parameters of Textured Images and Its Applications

Yue WU; Yasuo YOSHIDA

Rotation and Scaling Invariant Parameters of Textured Images and Its Applications

Yue WU, Yasuo YOSHIDA

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This paper presents a simple and efficient method for estimation of parameters useful for textured image analysis. On the basia of a 2-D Wold-like decomposition of homogenenous random fields, the texture field can be decomposed into a sum of two mutually orthogonal components: a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. The parameters representing the ellipse and those representing the delta functions can be used to detect rotation angles and scaling factors of test textures. Specially, rotation and scaling invariant parameters, which are applicable to the classification of rotated and scaled textured images, can be estimated by combining these parameters. That is, a test texture can be correctly classified even if it is rotated and scaled. Several computer experiments on natural textures show the effectiveness of this method.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.8 pp.944-950

Publication Date: 1995/08/25

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Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)

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Yue WU, Yasuo YOSHIDA, "Rotation and Scaling Invariant Parameters of Textured Images and Its Applications" in IEICE TRANSACTIONS on Fundamentals, vol. E78-A, no. 8, pp. 944-950, August 1995, doi: .
Abstract: This paper presents a simple and efficient method for estimation of parameters useful for textured image analysis. On the basia of a 2-D Wold-like decomposition of homogenenous random fields, the texture field can be decomposed into a sum of two mutually orthogonal components: a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. The parameters representing the ellipse and those representing the delta functions can be used to detect rotation angles and scaling factors of test textures. Specially, rotation and scaling invariant parameters, which are applicable to the classification of rotated and scaled textured images, can be estimated by combining these parameters. That is, a test texture can be correctly classified even if it is rotated and scaled. Several computer experiments on natural textures show the effectiveness of this method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_8_944/_p

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@ARTICLE{e78-a_8_944,
author={Yue WU, Yasuo YOSHIDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Rotation and Scaling Invariant Parameters of Textured Images and Its Applications},
year={1995},
volume={E78-A},
number={8},
pages={944-950},
abstract={This paper presents a simple and efficient method for estimation of parameters useful for textured image analysis. On the basia of a 2-D Wold-like decomposition of homogenenous random fields, the texture field can be decomposed into a sum of two mutually orthogonal components: a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. The parameters representing the ellipse and those representing the delta functions can be used to detect rotation angles and scaling factors of test textures. Specially, rotation and scaling invariant parameters, which are applicable to the classification of rotated and scaled textured images, can be estimated by combining these parameters. That is, a test texture can be correctly classified even if it is rotated and scaled. Several computer experiments on natural textures show the effectiveness of this method.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - Rotation and Scaling Invariant Parameters of Textured Images and Its Applications
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 944
EP - 950
AU - Yue WU
AU - Yasuo YOSHIDA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1995
AB - This paper presents a simple and efficient method for estimation of parameters useful for textured image analysis. On the basia of a 2-D Wold-like decomposition of homogenenous random fields, the texture field can be decomposed into a sum of two mutually orthogonal components: a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. The parameters representing the ellipse and those representing the delta functions can be used to detect rotation angles and scaling factors of test textures. Specially, rotation and scaling invariant parameters, which are applicable to the classification of rotated and scaled textured images, can be estimated by combining these parameters. That is, a test texture can be correctly classified even if it is rotated and scaled. Several computer experiments on natural textures show the effectiveness of this method.
ER -