On the analysis of canvas wrinkling via isogeometric stereocorrelation

Dufour, John-Eric; Gonnet, Emilie; Grau, Sébastien; Hild, François

doi:10.1016/j.ijsolstr.2017.06.037

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…46 During its restoration, NURBS-based stereocorrelation was employed to analyze two tensioning systems of the canvas in order to minimize wrinkling. 47 As stated above, the picture was converted to gray levels for the correlation analysis. No pattern was deposited and even though the contrast is rather low in comparison with speckle patterns (Figure 1), it was shown that very fine analyses could be preformed thanks to global NURBS-based analyses.…”

Section: Analysis Of Artificial Casesmentioning

confidence: 99%

Displacement uncertainties with multiview correlation schemes

Hild

Roux

2020

The Journal of Strain Analysis for Engineering Design

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Multiview correlation is based on the use of explicitly parameterizing surfaces whose deformation is to be measured. In the present analyses, the surfaces are modeled as triangular facets (for local approaches), and with finite-element meshes (for global approaches) using a linear interpolation between nodal displacements. Displacement uncertainty due to the presence of noise in the images is considered. The noise floor levels of the latter are compared for both local and global cases with two different sets of pictures. A priori estimates based on the Hessian of the registration procedure are derived analytically, and a simplified expression is provided that is shown to be in very good agreement with their a posteriori evaluations. The uncertainty difference between local and global approaches is interpreted from their different spatial resolutions.

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Section: Analysis Of Artificial Casesmentioning

confidence: 99%

Displacement uncertainties with multiview correlation schemes

Hild

Roux

2020

The Journal of Strain Analysis for Engineering Design

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“…In 2017, John-Eric Dufour et al. 8 put forward measuring wrinkling under various conditions of contrast and loading via isogeometric stereocorrelation.…”

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confidence: 99%

“…In 2014, Liu et al 7 proposed a multi-directional fabric wrinkle measurement method in which the standard deviation coefficients of wavelet decomposition and GLCM were used to characterize fabric wrinkle features. In 2017, John-Eric Dufour et al 8 put forward measuring wrinkling under various conditions of contrast and loading via isogeometric stereocorrelation.…”

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confidence: 99%

Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

Zhou

Zhang

et al. 2020

Textile Research Journal

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Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized; that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.

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On the analysis of canvas wrinkling via isogeometric stereocorrelation

Cited by 2 publications

References 32 publications

Displacement uncertainties with multiview correlation schemes

Displacement uncertainties with multiview correlation schemes

Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

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