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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".