Comparison of linear and non-linear calibration methods for phase-shifting surface-geometry measurement
Bibliographic record
Abstract
In fringe-projection surface-geometry measurement, phase unwrapping techniques produce a continuous phase distribution that contains the height information of the 3-D object surface. To convert the phase distribution to the height of the 3-D object surface, a phase-height conversion algorithm is needed, essentially determined in the system calibration which depends on the system geometry. Both linear and non-linear approaches have been used to determine the mapping relationship between the phase distribution and the height of the object; however, often the latter has involved complex derivations. In this paper, the mapping relationship between the phase and the height of the object surface is formulated using linear mapping, and using non-linear equations developed through simplified geometrical derivation. A comparison is made between the two approaches. For both methods the system calibration is carried out using a least-squares approach and the accuracy of the calibration is determined both by simulation and experiment. The accuracy of measurement using linear calibration data was generally higher than using non-linear calibration data in most of the range of measurement depth.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".