%0 Journal Article
%T Assessment of Digital Image Correlation Measurement Accuracy in the Ultimate Error Regime: Improved Models of Systematic and Random Errors
%+ Laboratoire Navier (navier umr 8205)
%+ Institut Pprime [UPR 3346] (PPrime [Poitiers])
%+ Centre de recherche sur les Ions, les MAtériaux et la Photonique (CIMAP - UMR 6252)
%+ Institut Clément Ader (ICA)
%+ Institut Pascal (IP)
%+ Laboratoire de Mécanique et Génie Civil (LMGC)
%+ ThermoMécanique des Matériaux (ThM2)
%A Bornert, Michel
%A Doumalin, Pascal
%A Dupré, Jean-Christophe
%A Poilâne, Christophe
%A Robert, Laurent
%A Toussaint, Evelyne
%A Wattrisse, Bertrand
%< avec comité de lecture
%Z KTK-3564
%Z DOCA-01
%Z MICS
%@ 0014-4851
%J Experimental Mechanics
%I Society for Experimental Mechanics
%V 58
%N 1
%P p.33-48
%8 2018
%D 2018
%R 10.1007/s11340-017-0328-5
%K Ultimate error regime
%K Error assessment
%K Digital image correlation
%K Prediction models
%K Uncertainty quantification
%Z Engineering Sciences [physics]/Mechanics [physics.med-ph]
%Z Engineering Sciences [physics]/Optics / Photonic
%Z Engineering Sciences [physics]/Signal and Image processingJournal articles
%X The literature contains many studies on assessment of DIC uncertainties, particularly in the ultimate error regime, when the shape function used to describe the material transformation perfectly matches the actual transformation. For pure sub-pixel translations, bias and random errors obtained for experimental or synthetic images show more complex evolution versus the fractional part of displacement than those predicted by the existing theoretical models. Indeed, small deviations arise, mainly around integer values of imposed displacements for noisy images, and they are interpreted as the unrepresentativeness of the underlying hypotheses of the theoretical models. In a first step, differences between imposed and measured displacements are analysed: random error is independent of fractional displacement, and systematic error does not decrease for values close to integer displacements whatever the noise level. Therefore, new prediction models are proposed based on the analysis of identified phenomena from synthetic speckle-pattern 8-bit images. The statistical approach used in this paper generalizes the methods proposed in the literature and mimics the experimental methodology usually used for displacement measurements performed in different subsets in the same image. Two closed-form expressions for the systematic and random errors and a linear interpolation scheme are developed. These models, depending only on image properties and the imposed displacement, are built with a very limited number of parameters. It is then possible to predict the evolution of bias and random errors from one to four images.
%G English
%2 https://hal.science/hal-01577239/document
%2 https://hal.science/hal-01577239/file/Art_Wattrisse_al_Assessment_digital_image_2017.pdf
%L hal-01577239
%U https://hal.science/hal-01577239
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%~ INSTITUT-TELECOM
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