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Theoretical Basis for Optical Granulometry by Wavelet Transformation

Hryciw, Roman D. / Ohm, Hyon-Sohk / Zhou, Jie

A theoretical framework for optical granulometry of sands by a wavelet transformation method is presented. The Haar wavelet transformation is explained by using simple mathematics with comparisons drawn to the use of wavelet decomposition in image compression and image enhancement. The use of seven wavelet decomposition levels on thousands of $128 \times 128 pixel$ areas of a much larger image of a soil specimen presorted by size yields the soil’s particle-size distribution. The method employs previously developed calibration curves of a wavelet index (CA) versus the average number of pixels per particle diameter (PPD). The curves are nonlinear in CA versus $\log_{10} (PPD)$ space. This paper demonstrates that the nonlinearity stems from having to use a relatively low number of decomposition levels. With future advances in camera technology, a linear CA versus $\log_{10} (PPD)$ calibration curve could be developed requiring a single soil texture index ( $T$ ) parameter.

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Theoretical Basis for Optical Granulometry by Wavelet Transformation

Hryciw, Roman D. / Ohm, Hyon-Sohk / Zhou, Jie

A theoretical framework for optical granulometry of sands by a wavelet transformation method is presented. The Haar wavelet transformation is explained by using simple mathematics with comparisons drawn to the use of wavelet decomposition in image compression and image enhancement. The use of seven wavelet decomposition levels on thousands of $128 \times 128 pixel$ areas of a much larger image of a soil specimen presorted by size yields the soil’s particle-size distribution. The method employs previously developed calibration curves of a wavelet index (CA) versus the average number of pixels per particle diameter (PPD). The curves are nonlinear in CA versus $\log_{10} (PPD)$ space. This paper demonstrates that the nonlinearity stems from having to use a relatively low number of decomposition levels. With future advances in camera technology, a linear CA versus $\log_{10} (PPD)$ calibration curve could be developed requiring a single soil texture index ( $T$ ) parameter.

Theoretical Basis for Optical Granulometry by Wavelet Transformation

Hryciw, Roman D. / Ohm, Hyon-Sohk / Zhou, Jie

A theoretical framework for optical granulometry of sands by a wavelet transformation method is presented. The Haar wavelet transformation is explained by using simple mathematics with comparisons drawn to the use of wavelet decomposition in image compression and image enhancement. The use of seven wavelet decomposition levels on thousands of $128 \times 128 pixel$ areas of a much larger image of a soil specimen presorted by size yields the soil’s particle-size distribution. The method employs previously developed calibration curves of a wavelet index (CA) versus the average number of pixels per particle diameter (PPD). The curves are nonlinear in CA versus $\log_{10} (PPD)$ space. This paper demonstrates that the nonlinearity stems from having to use a relatively low number of decomposition levels. With future advances in camera technology, a linear CA versus $\log_{10} (PPD)$ calibration curve could be developed requiring a single soil texture index ( $T$ ) parameter.

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Title:

Theoretical Basis for Optical Granulometry by Wavelet Transformation

Contributors:

Hryciw, Roman D. (author) / Ohm, Hyon-Sohk (author) / Zhou, Jie (author)

Published in:

Journal of Computing in Civil Engineering ; 29

Publication date:

2013-07-25

ISSN:

0887-3801 , 1943-5487

DOI:

https://doi.org/10.1061/(ASCE)CP.1943-5487.0000345

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

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