Adaptive image thresholding for real‐time particle monitoring
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 μm2. 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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".