Image‐based Continuous Displacement Measurements Using an Improved Spectral Approach
Bibliographic record
Abstract
ABSTRACT Digital Image Correlation algorithms capable of determining continuous displacement fields are receiving growing attention in the field of mechanical properties identification. In this paper, we develop an Improved Spectral Approach (ISA) to reconstruct continuous displacements based on their Fourier decomposition. This approach leads to a time and memory‐efficient algorithm, thanks to the fast Fourier transform. Moreover, the Fourier‐based decomposition enables accurate heterogeneous measurements. Improvements consist in increasing the accuracy and convergence rate as well as dealing with non‐periodic displacements and images. Furthermore, a theoretical framework is presented to quantify the noise sensitivity of the ISA from which useful information is retrieved. The approach is evaluated using synthetic images deformed by heterogeneous displacement fields. Comparisons show that the introduced modifications lead to lower uncertainties by one order of magnitude in the case of non‐periodic images and displacement field studied. Moreover, first‐order (SO1) and second‐order (SO2) subset‐based Digital Image Correlation algorithms are compared with the ISA. The comparisons herein reveal that the uncertainties of the ISA are 6–9 times smaller than those of the SO1 due to insufficiency of the first‐order shape function for the estimation of heterogeneous displacements, while being slightly smaller than those of the SO2. Moreover, as the image smoothness decreases, the uncertainties of the SO2 deviate from those of the ISA and the exact displacements. The presented approach shows great potentials for challenging applications such as strain measurements at microstructural levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".