Multiscale model-based feature extraction in structural texture images
Bibliographic record
Abstract
We deal with the problem of time-efficient extraction of structural features in a large class of structural texture images. The proposed approach of multiscale morphological texture modeling describes explicitly and concisely both shape and intensity parameters in the structural texture model. The modeling is based on a morphological skeletal representation of structural texture cells as objects of interest and the genomic growth of a texture region starting from a seed cell. This representation offers the advantage of concise description of texture cells as compared to the existing edge-based or contour-based approaches. A computationally efficient estimation of the structural texture parameters for texture segmentation tasks is proposed. The model parameter estimation and subsequent feature extraction rely on cell localization and scale-based locally adaptive binarization of the localized cells using isotropic matched filtering. The multiscale isotropic matched filter (MIMF) provides a scale- and orientation-invariant detection of structural cells regarded as multiple objects of interest in texture regions. Results of experiments pertaining to the parameter estimation from synthetic and real texture images as well as the segmentation of texture regions based on structural features are also provided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".