Performance Analysis of Oriented Feature Detectors
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
Oriented feature detectors are fundamental tools in image understanding, as many images display relevant information in the form of oriented features. Several oriented feature detectors have been developed; some of the important families of oriented feature detectors are steerable filters and Gabor filters. In this work, a performance analysis is presented of the following oriented feature detectors: the Gaussian second-derivative steerable filter, the quadrature-pair Gaussian second-derivative steerable filter, the real Gabor filter, the complex Gabor filter, and a line operator that has been shown to outperform the Gaussian second-derivative steerable filter in the detection of linear structures in mammograms. The detectors are assessed in terms of their capability to detect the presence of oriented features, as well as their accuracy in the estimation of the angle of the oriented features present in the image. It is shown that the Gabor filters yield the best detection performance and angular accuracy, whereas the steerable filters have the best performance in terms of computational speed.
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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 | 0.002 |
| 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) | 0.000 | 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