Advances in Two-Dimensional and Three-Dimensional Computer Vision

By Michael A. Sutton, S. Mcneill, J. Helm, Y. Chao. 2000

583 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5bf2c8bb1d7fc38b743c0e175f991d6ec9e6fb6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Bing Pan
2. François Hild
3. Stéphane Roux
4. J. Réthoré
5. R. Brault
6. S. Mistou
7. H. Welemane
8. Elodie Péronnet

In Computer Vision and Pattern Recognition

1. Bing Pan
2. François Hild
3. Stéphane Roux
4. J. Réthoré
5. Zhaoyang Wang
6. Huimin Xie
7. S. Mistou
8. M. Fazzini

In Media Technology

1. Bing Pan
2. François Hild
3. Stéphane Roux
4. Zhaoyang Wang
5. J. Réthoré
6. Huimin Xie
7. S. Mistou
8. M. Fazzini

Citations in the corpus

• Stress intensity factor measurements from digital image correlation: post-processing and integrated approaches (2006, background): “For instance, stress intensity factors (McNeil, et al., 1987; R´´ et al., 2005), crack tip opening angles (Dawicke and Sutton, 1994) or crack tip opening displacements ( Sutton et al., 2000 ), and toughness (Forquin et al., 2004) can be measured with a very good accuracy by means of digital image co”
• Extended digital image correlation with crack shape optimization (2008, methodology): “Digital image correlation is now a quite well established tool for measuring displacement fields [22].”
• Equivalence of digital image correlation criteria for pattern matching. (2010, methodology): “In literature, various criteria, including cross-correlation (CC) criterion, sum of absolute difference (SAD), sum of squared difference (SSD), and parametric sum of squared difference (PSSD) have been designed and used [4,6,9 – 17].”
• Performance of sub-pixel registration algorithms in digital image correlation (2006, background): “Due to widespread applications in experimental mechanics and other scientific research [1], it is no longer necessary to emphasize the importance of digital image correlation (DIC) here.”
• Stress Intensity Factor Gauging by Digital Image Correlation: Application in Cyclic Fatigue (2007, unknown): “Among various techniques, Digital Image Correlation (DIC) has already been applied to analyze cracks in homogeneous and heterogeneous materials [9].”
• On the narrow dip structure at 1.9 GeV/c2 in diffractive photoproduction (2003, unknown): “Digital image correlation is a technique that consists in measuring displacement fields based on image pairs of the same specimen at different stages of loading [27].”
• Use of digital image correlation to measure large-strain tensile properties of ductile thermoplastics (2009, methodology): “Unlike 2D DIC systems which don’t require any calibration for strain calculations, 3D DIC systems require a system calibration for both strain and displacement calculations [10].”
• Damage investigation in CFRP composites using full-field measurement techniques: Combination of digital image stereo-correlation, infrared thermography and X-ray tomography (2013, background): “bly one of the most popular due to its implementation ease and development of commercial softwares [19].”
• Self-Assembled Nanoparticle Surface Patterning for Improved Digital Image Correlation in a Scanning Electron Microscope (2013, unknown): “Existing techniques include photolithography [3, 5], thin film ablation [6], electron-beam (e-beam) lithography [7-9] and nanoparticles [7, 10, 11].”