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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".