Pre-correction of projected gratings for surface profile measurement
Bibliographic record
Abstract
This paper discusses errors caused by unequal grating pitch in applying the phase-shifted digital grating projection method for object profile measurement. To address the related issues, a new scheme is proposed to effectively improve the uniformity of the projected grating pitch across the object surface with no additional hardware cost. The improvement is mainly realized via a grating pitch pre-correction algorithm assisted by Digital Speckle/Image Correlation (DSC/DIC). DIC is utilized to accurately determine the surface grating pitch variation when an originally equal-pitched grating pattern is slant projected to the surface. With the actual pitch distribution function determined, a pre-corrected grating with unequal pitch is generated and projected, and the iterative algorithm reaches a constant pitched surface grating. The mapping relationship between the object surface profile (or out-of-plane displacement) and the fringe phase changes is obtained with a real-time subtraction based calibration. A quality guide phase unwrapping method is also adopted in the fringe processing. Finally, a virtual reference phase plane obtained by a 3-point plane fitting algorithm is subtracted to eliminate the carrier phase. The study shows that a simple optical system implemented with the mentioned improvements remarkably increase the accuracy and the efficiency of the measurement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".