Bibliographic record
Abstract
Processing of image windows rather than complete images is useful for robots incorporating visual servoing as high image processing rates are required. Each image window is segmented, often by thresholding, to identify features of interest. To adapt to changing conditions and to achieve the thresholding of low contrast and shadowed windows, a sophisticated method for performing dynamic segmentation is required. Segmentation methods from the pattern recognition and optical character recognition fields were studied to determine their effectiveness at thresholding 32 by 32 pixel image windows of circular hole features. The segmentation technique must be capable of preserving the centroid location with sub-pixel accuracy. To this end a new morphological preprocessing method is introduced to improve the performance of most thresholding algorithms. It was found that this new preprocessing method was able to improve the centroid location error by nearly 40% when Yasuda's thresholding algorithm was used. The preprocessing algorithm in combination with Yasuda's thresholding algorithm was able to segment the holes with an average centroid location error of 0.423 pixels and a standard deviation of 0.328 pixels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".