Application of Triple Features Theory to the Analysis of Half-Tone Images and Colored Textures. Feature Construction by Virtue of Stochastic Geometry and Functional Analysis
Why this work is in the frame
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Bibliographic record
Abstract
The existing methods of half-tone or color image recognition generally presuppose a prior simplification of the object to analyze. Such a simplification normally involves image binarization which may result in a loss of essential elements of information on the object. The paper proposes a new approach towards half-tone images and colored textures analysis and recognition by virtue of stochastic geometry and functional analysis. The method makes it possible to form both the recognition features to typify image geometric singularities, and the recognition features to reflect image brightness and color characteristics. According to the method suggested, recognition features can be generated in abundance - thousands of them - in an unattended mode, which provides for a most reliable image recognition. Moreover, the resulting features prove invariant both to a group of motions and to linear deformations, which is the key to the better part of image recognition problems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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