Particulate material fabric characterization by rotational haar wavelet transform

Zheng, Junxing; Hryciw, Roman D.

doi:10.1016/j.compgeo.2017.02.021

Cited by 27 publications

(9 citation statements)

References 64 publications

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“…Therefore, the stronger fabric anisotropy occurred at higher relative density D r . This observation agree with Zheng and Hryciw [33,34]. Anisotropic strength of sands for σ 3 > 400 kPa and D r < 50% are not clear because of no experimental data available.…”

Section: Anisotropic Strength Observationssupporting

confidence: 90%

Simplified approach to characterize anisotropic strength of granular soils

Sun

Zheng

2020

Geo-Engineering

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Experimental evidence shows that the strength of granular soils is significantly influenced by inherent cross anisotropy which cannot be properly described by isotropic failure criteria. This paper reviewed laboratory test results of various sands at different fabric directions. Based on the observations, this paper formulates the hypothesis that deposit plane creates a plane of weakness and the anisotropic strength of sands depends on orientations of deposit plane and failure plane. The strength decreases when orientations of deposit plane and failure plane are close to each other, and the strength increase when they diverge from each other. Then, an anisotropic failure criterion is developed based on this hypothesis and validated by available experimental data from literature. Remarkable agreements between predictions and measurements have been observed, which demonstrate validity, effectiveness, and robustness of new criterion in characterizing anisotropic strength of sands with variations of loading directions and intermediate principal stresses.

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Section: Anisotropic Strength Observationssupporting

confidence: 90%

Simplified approach to characterize anisotropic strength of granular soils

Sun

Zheng

2020

Geo-Engineering

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“…22 Through the trial test, the wavelet basic function has no substantial impact on the result of color transfer. In this paper, one-level wavelet transform using Haar basic function 23 was used to filter the redundant high-frequency information and to avoid omitting the details. Taking the grayscale image of Figure 3(b) as the example, Figure 5 illustrates the one-level Haar wavelet transform decomposition.…”

Section: Wavelet Transformmentioning

confidence: 99%

Appearance change for colored spun yarn fabric based on image color transfer

Zhang

Wang

et al. 2021

Textile Research Journal

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The fabric of colored spun yarn has ever-changing appearances and styles with different fancy yarns. The fabric image is commonly designed by the designer using the software, which needs complex user interactions and difficult image segmentation. In this paper, a modified color transfer method was proposed to generate the fabric appearance of colored spun yarn. Given the color card as the target image, the style fabric image was matched as the reference image based on the dominant luminance. After transferring the two images to lαβ color space, Wavelet transform and luminance sampling were utilized to filter the redundant high-frequency information and select the representative pixels, respectively. Then, the chromatic channels were transferred based on the best matched luminance and the neighborhood relation. Finally, the image after color transfer was reconstructed by wavelet reconstruction. The proposed reference image matching maintained the result to be the ground truth. For the samples selected, the combined methods based on wavelet transform and luminance sampling improved the efficiency and performance of the proposed scheme. Experiments were conducted on different fabrics with different colors and styles. Experiments demonstrated the validity and superiority of the proposed method, which can provide referential assistance for the designer and save considerable amounts of labor.

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“…The multi‐scale representation of images method, such as wavelet transform (WT), à trous wavelet transform and contourlet transform, can provide a coherent framework for the multi‐scale representation of remote sensing images. For the WT, it first extracts spatial information from a high‐resolution pan image and injects the spatial information into the MS bands to improve the spatial resolution and reduce the color distortion (Mukherjee et al 2016; Janeliukstis et al 2017; Zheng and Hryciw 2017).…”

Section: Introductionmentioning

confidence: 99%