Image Correlation for Shape, Motion and Deformation Measurements

By Michael A Orteu, J. Schreier, H. Springer. 2009

3,984 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/89d484cdd004883fce19f58f7080a4a3dd6a9ef8

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

1. Bing Pan
2. François Hild
3. Stéphane Roux
4. Wei Tong
5. K. Li
6. Zhaoyang Wang
7. Hugo Leclerc
8. Fabrice Pierron

Citations in the corpus

• Fast, Robust and Accurate Digital Image Correlation Calculation Without Redundant Computations (2013, methodology): “[3], the IC-GN algorithm was used by the authors for full-field displacement measurement.”
• Comparison of Local and Global Approaches to Digital Image Correlation (2012, methodology): “The technique has reached centipixel standard resolutions and uncertainties [13–16], and millipixel biases or less [2, 14, 16, 17] that are compatible with a large variety of cases encountered in solid mechanics [1–3].”
• Determination of mechanical properties and full-field strain measurements of rock material under dynamic loads (2013, methodology): “The search to find the best match between subsets is accomplished using an optimization method called the Newton–Raphson method [18,19,48].”
• Sub-Grain Scale Digital Image Correlation by Electron Microscopy for Polycrystalline Materials during Elastic and Plastic Deformation (2016, methodology): “The software parameters used include a zero-normalized squared difference correlation criterion for correlation and a 5×5 subset decay kernel matrix (minimum decay filter available) for deriving strain values [30].”
• Estimation of Elastoplastic Parameters via Weighted FEMU and Integrated-DIC (2015, methodology): “Digital Image Correlation is a widely used displacement eld measurement technique [52, 32].”
• Analysis of image series through global digital image correlation (2012, background): “A general theoretical framework is presented, tested on artificial and experimental image series.”