A Quantitative Study of Illumination Techniques for Machine Vision Based Inspection
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, three basic lighting geometries are compared quantitatively in an inspection task that checks for the presence of J-clips on an aluminum carrier. Two independent LabVIEW® machine vision algorithms were used to evaluate backlight, bright field and dark field illumination on their ability to minimize variations within a pass (clip present) or fail (clip absent) sample set, as well as maximize the separation between sample sets. Results showed that there were clear differences in performance with the different lighting geometries, with over a 30% change in performance. Although it is widely acknowledged that the choice of lighting is not a trivial exercise for machine vision systems, this paper provides a case study of the quantitative performance of different lighting geometries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it