Bibliographic record
Abstract
Measuring objects that have highly reflective surfaces with structured light (SL) is challenging. In traditional high dynamic range (HDR)-based technique, cumbersome exposure selection strategies are employed. This thesis proposes two novel SL-based methods to measure parts with highly reflective surfaces. First, an image enhancement-based method with only single exposure is developed. A new quantitative metric and a skip pyramid context aggregation network (SP-CAN) are proposed to select and enhance ingle-exposure images. Second, an automated exposure selection method is designed to improve the traditional HDR method. A new image quality metric is designed to evaluate captured images, based on which a multiple-exposures selection strategy is developed. Experimental results demonstrated that the first method can achieve 97.6% coverage rate and 0.040 mm measurement accuracy using only 0.6 second, while the second method can achieve a surface coverage rate of 97.4% and measurement accuracy 0.043 mm with only 3-4 exposures averagely (3.3 s).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".