Bibliographic record
Abstract
In mathematical morphology, images are represented with sets. On binary images, sets delineate portions in the image plane, creating binary shapes. Measurements can be done on these shapes. It has been demonstrated that, for digital images, there exist very few basic measurements. These measurements, along with image transformations, generate all the possible measurements that can be done on an image. For binary images, all the measurements are based on the area, perimeter and connectivity number (number of connected components minus the number of holes inside them). Gray-tone images can also be modeled as 3-D sets. However the 5-D space is not homogeneous: units along the image plane are not the same as those on the intensity axis. This causes problems because not all the basic measurements are physically valid. The basic measurements on gray-tone images are the volume, surface, norm (or mean curvature) and the connectivity number. In this paper, we present a criterion to assess the physical validity of the basic measurements. This allows us to further limit the number of useful basic measurements. On gray-tone images, these are the volume and the connectivity number. We illustrate our findings with an experiment using physically valid and invalid measurements. These measurements are applied to texture characterization and segmentation. We study the behavior of these measurements under illumination changes. We show that a physically invalid measurement gives erratic answers under such circumstances. This has important consequences on the robustness of image analysis algorithms.>
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".