5 - Gradient couleur multiéchelle pour la segmentation d'images
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
This paper presents a new gradient model for video color images. These multispectral images have the characteristic, either for the transmission or for storage, to present a reduced bandwidth of color components compared to that of luminosity. The use of traditional methods of determination of the multispectral gradient amplifies the noise from the color components. We adapt the vector gradient from Lee and Cok [22], and introduce the computation of the partial derivatives at different scales according to the resolution of each component. We show that a weight is necessary between the derivatives of color and luminosity components to obtain the multiscale color gradient (MCG). The application of the MCG on microscopic color images illustrates the advantages of our method. The contribution of the MCG is shown with results of edge detection from the gradient image. Finally, segmentation by active contours of crystals in microscopic images of cement clinker (industrial application) is realized using the MCG image.
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) | 0.001 | 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 it