Dynamic phase measurement by clustering method
Bibliographic record
Abstract
Phase measurement is a key step in quantitative optical metrology. While phase shifting technique is widely applied for accurate and reliable static or semi-static phase measurement, Fourier and wavelet transforms are often employed for high speed dynamic phase measurement. In our previous papers, the authors had proposed an alternative clustering method for dynamic phase measurement. The proposed method utilizes the phase clustering effect and the prior knowledge of the speckle field to extract the deformed phase map from one single deformed speckle pattern. The clustering method, however, may fail at area with abundant noise and large phase gradient. In this paper, we improve the clustering method by incorporating an advanced phase filtering methods for wrapped phase filtering. The reconstructed wrapped phase map is with very good quality and ready for phase unwrapping with any simple unwrapping algorithms. The basic ideas and the implementation approach will be described in details. Several examples based on shearography and holographic interferometry will be presented. Comparisons between the proposed method and phase shifting method will be made. The results demonstrate the accuracy and robustness of the integrated dynamic phase extraction method. The integrated phase retrieval method proposed here has great potential to simplify optical setup for dynamic phase 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".