Adaptive image thresholding for real‐time particle monitoring
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Image thresholding is critical to computer vision systems designed to detect very small numbers of contaminant particles from analysis of images acquired by in‐line process monitoring. The objective of this work was to obtain a thresholding method that would permit in‐line, “real‐time,” determination of both the number of particles in an image and their size. An additional requirement was that it automatically adapt to inevitable variations in the image quality. A new global image thresholding method, the MaxMin method (“MaxMin”), was developed. MaxMin notes the size of the smallest detected particle in an image as threshold value is progressively changed from black to white. The selected threshold value is the one providing the largest size. MaxMin was tested on thousands of images, and it was shown to readily adapt to images of different background noise levels and provided particle counts as accurate as those of a human observer in less than three seconds per image. Error in particle size measurement was a function of the particle size and the image resolution. It was about 3% for 50 μm particles, using a CCD camera with 2× lens, calibrated for each pixel to represent ∼5 μm 2 . The error was significantly higher for smaller particles, when the same system resolution was used. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 9–14, 2006
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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 | 0.001 |
| 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