Image stitching by means of adaptive normalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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