Saturation avoidance by adaptive fringe projection in phase-shifting 3D surface-shape measurement
Bibliographic record
Abstract
Fringe-pattern projection systems are capable of non-contacting 3D surface full-field measurement with high accuracy. However, the systems are prone to intensity saturation and low signal-to-noise ratio (SNR) when measuring objects with a large range of reflectivity across the surface. Intensity saturation occurs when the light intensity directed to the camera exceeds the maximum intensity quantization level. A low SNR occurs when there is a low intensity modulation compared to the amount of noise in the image. Saturation and low SNR can result in significant measurement error. This paper presents a method for saturation avoidance during object-surface measurement, by adaptively adjusting the projected fringe-pattern intensities, through the maximum input gray level (MIGL). A high SNR can be maintained while avoiding saturation by combining the intensities from phase-shifted images captured at different MIGL, into a set of composite phase-shifted images. In measurement of a black and white checkerboard at different depths, the newly developed method reduced errors by an average 0.25 mm compared to the highest accuracy measurement using a uniform MIGL.
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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".