1 - Classification de textures en imagerie sonar et invariance en rotation
Bibliographic record
Abstract
This paper addresses the automatic cartography of sea-bottom by means of high resolution sonar images. Many texture analysis methods have been developed since now, based on statistical, geometrical or spectral modeling [14, 45, 7, 44]. Nevertheless, few of them are robust toward image rotations. Indeed, the property of rotation invariance is essential in our framework, particularily for obtaining good classification rates. We present in this article five methods for the automatic classification of rotating images, corresponding to four classes of sea-floor: “sand”, “ridge”, “dune” and “wreck”. The first one is an extension of a circular AutoRegressive method, initially proposed by Kashyap et Khotanzad [19], which allows to estimate a reduced number of rotation invariant parameters. The four other methods are based on an original approach, consisting to apply a mathematical transform to a set of parameters describing texture features. Two of them consist in computing the Log- Polar transform to autoregressive (AR) or correlation (COR) parameters. The two others consist in estimating the Zernike moments of autoregressive (AR) or correlation (COR) parameters. Classification rates obtained on sonar images and on Brodatz album are presented and allow to compare the performances of each approach.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".