Modified sinusoidal fringe-pattern projection for variable illuminance in phase-shifting three-dimensional surface-shape metrology
Bibliographic record
Abstract
A camera-independent method of avoiding image saturation using modified sinusoidal fringe-pattern projection to reduce surface measurement error and thus accommodate variable illuminance in phase-shifting surface-shape measurement is presented. The maximum input gray level (MIGL) in the projected patterns is reduced to an optimal tradeoff point, below which the intensity modulation, contrast, and signal-to-noise ratio would diminish the advantage of further MIGL reduction. Measurement simulations using 31 MIGL values, from 105 to 255 in increments of 5, demonstrated reductions in root-mean-square errors for ambient illuminance of 400, 500, 600, 700, 800, and 900 lx, from 0.38, 0.56, 0.86, 1.21, 85, and 373 mm, respectively, at 255 MIGL, to 0.31 to 0.32 mm at the optimum MIGL. The advantage of the method was confirmed in real measurements of a flat plate and human masks. The ability to perform camera-independent measurements under variable lighting conditions and surface reflectivity may lead to more practical measurements in uncontrolled environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".