Comparison of Local and Global Approaches to Digital Image Correlation
By François Hild, Stéphane Roux. 2012
414 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/a197014f2c1f4645589278a226be99c83609c255
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- François Hild
- Stéphane Roux
- Bing Pan
- Hugo Leclerc
- Michel Grédiac
- F. Mathieu
- John-Eric Dufour
- Benoît Blaysat
In Media Technology
- Bing Pan
- François Hild
- Stéphane Roux
- Hugo Leclerc
- John-Eric Dufour
- F. Mathieu
- N. Swiergiel
- G. Besnard
Citations in the corpus
- Analysis of image series through global digital image correlation (2012, background): “Spatial regularization is a way to reduce the number of degrees of freedom by additional constraints [35].”
- Estimation of Elastoplastic Parameters via Weighted FEMU and Integrated-DIC (2015, methodology): “Since displacement measurements are corrupted by noise, the DIC matrix is also of importance when evaluating the covariance matrix [Cu(t)] of the measured degrees of freedom [8, 31]”
- Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals (2018, background): “However, some researchers argued that FE-based global DIC can offer better measurement accuracy due to the enforced continuity between element nodes [221].”
- The Grid Method for In‐plane Displacement and Strain Measurement: A Review and Analysis (2016, background): “Assessing the errors made while measuring displacement or strain components is addressed in several papers on DIC, see [149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 100, 101] for instance.”
- Shape, displacement and mechanical properties from isogeometric multiview stereocorrelation (2015, methodology): “The chi-squared error is based on the [C] matrix resulting from the assembly of all elementary matrices according to the chosen formulation as described in Appendix B, Equation (27), as it can be shown that it is proportional to the inverse covariance matrix of the parameters {P } for a white noise ”