Bibliographic record
Abstract
Image stitching — the process of amalgamation of separate image fragments to form a complete representation of the entire scene — might become quite a challenging problem in the presence of non-additive noises and/or brightness variability artifacts. An additional degree of complication may further be inflicted in situations when one is dealing with large size data, as it is usually the case in tiling microscopy. To overcome such difficulties, a novel approach to the problem of image stitching is proposed here. In the heart of the proposed solution is Wallis filtering, which is a standard tool of image processing used for adaptive contrast adjustment and local image normalization. More importantly, Wallis filtering allows representing a given image in terms of its normalized version and associated local statistics. Subsequently, we show that stitching the output of the Wallis filter is a much simpler and much more stable task as compared to stitching the images in their original domain. The proposed method has an additional advantage of being computational efficient, which is particularly important in tiling microscopy, where a typical height/width of data images is on the order of tens of thousands of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".