Segmentation of non-natural objects in landscape images using ridgelet transform
Why this work is in the frame
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Bibliographic record
Abstract
This study reports about the detection of non-natural structures in outdoor natural scenes. In particular, we present a new approach based on ridgelet transform for the segmentation of man-made objects in landscape scenes. Multiscale directional moments of ridgelet coefficients are used as features along with a principal component analysis (PCA) followed by a linear discriminant analysis (LDA), kernel-based LDA (KLDA), or support vector classifier (SVC). The statistical learning is done on about 3,000 image patches that represent natural and artificial content. Performances are measured in terms of image patch type classification (natural versus non-natural) and man-made object segmentation on two different image test sets. Results using ridgelets are compared to Gabor features. Altogether, we compare performance for six different feature/classifier combinations: ridgelets+LDA, ridgelet+KLDA, ridgelets+SVC, Gabor+LDA, Gabor+KLDA, and Gabor+SVC, and various external parameter values. Results show that most of the time, the combinations with ridgelets provide comparable or better performance.
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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 | 0.000 | 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 | 0.001 |
| 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