2008
DOI: 10.3166/remn.17.857-868
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Estimation of the strain field from full-field displacement noisy data

Abstract: RÉSUMÉ. Cette étude s'intéresse à la reconstruction de champs de déformation à partir de me-

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Cited by 53 publications
(29 citation statements)
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“…The filtering procedure is a local fit of a second degree polynomial using a specific weighing function. This is referred to as diffuse approximation and details can be found in [12]. The strains are then obtained directly from the polynomial coefficients.…”
Section: Methodsmentioning
confidence: 99%
“…The filtering procedure is a local fit of a second degree polynomial using a specific weighing function. This is referred to as diffuse approximation and details can be found in [12]. The strains are then obtained directly from the polynomial coefficients.…”
Section: Methodsmentioning
confidence: 99%
“…By using an identical setup rotated by 90 • one obtains the other component of in-plane displacement. The displacement resolution is close to 0.1 m for a spatial resolution of about 16 m. Strains were obtained from displacements using a diffuse approximation filtering (radius = 20 pixels, 248 m resolution) that provides excellent reconstruction/filtering compromise as described in [16,17]. The strain resolution is 2 × 10 −5 .…”
Section: Mechanical Characterisationmentioning
confidence: 99%
“…Here, polynomial basis of degree 2 is chosen, because, it appeared in [7] it was a good compromise between filtering and approximation error. With such a basis, the terms a 2 (x) and a 3 (x) represent the first order derivatives at point x in a diffuse way.…”
Section: The Diffuse Approximation (Da) As a Filtering Toolmentioning
confidence: 99%
“…It is reconstructed for the same filtering level, with both spatial Diffuse Approximation and Space-time Diffuse Approximation. One can note an improvement of the spatial resolution when using the Space-time 10005-p. 7 Diffuse Approximation. Furthermore, when comparing the strain field with the linear approximation of Figure 4(d), it seems the Space-time Diffuse Approximation yields a better spatial resolution.…”
Section: Comparing Spatial Diffuse Approximation and Space-time Diffumentioning
confidence: 99%