Bibliographic record
Abstract
A hierarchical multi-phase image segmentation using the original and a modified Chan-Vese 2-phase method is considered. A method of capturing features inside a pre-selected region of interest (ROI) is proposed that effectively restricts the segmentation operation to the ROI. At the first step, a modified image is created by setting the portion of the image outside the ROI to a uniform intensity equal to the mean image intensity inside the ROI. Effectively, this procedure partitions the initial image into two phases, in such a way that the ROI effectively becomes a 'segmented' feature. At the second step, the segmentation procedure is applied to the modified image, partitioning the image in two phases - object and background - inside the ROI. By confining segmentation to the ROI, it is shown, using an artificial image, that objects can be discriminated that could not have been found if segmentation had been performed on the entire image. If necessary, this second step can be repeated to further segment features of interest within the ROI, thereby providing a multi-phase segmentation procedure. ROI placement around features of interest requires prior knowledge, and may be derived from an atlas or manually prescribed by the operator. In this way, segmentation is possible on low-contrast features of interest, while ignoring features irrelevant for a particular application. Examples are provided for segmentation of several 2D/3D images performed both on entire images and inside a ROI.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".