Small infrared target detection using two-dimensional fast orthogonal search (2D-FOS)
Why this work is in the frame
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Bibliographic record
Abstract
It is well known that the overall performance of the automatic imaging target recognition system is strongly affected by the used detection technique. Recently, the wavelet and matching pursuit methods are merged together as an excellent methodology for detecting targets in a sequence of infrared images with high detection rate, and low false alarms. The wavelet transform is used as a detector of the regions of interest, which may include false alarms, while the matching pursuit uses the known target's features to reduce the clutters (or false alarms) from the wavelet output. Only the non-orthogonal matching pursuit is used for this purpose because its orthogonal version is more computationally expensive. This prevents the exploitation of the orthogonal matching pursuit, which can provide image modeling with less number of terms that can significantly faciliate the target extraction and clutter reduction. In this paper, we introduce the usage of the fast orthogonal search method, which is an orthogonal modeling technique, instead of matching pursuit for small infrared imaging target detection. The fast orthogonal search performs the orthogonalization process in more efficient way, so its computational time is much less than the original orthogonal matching pursuit. Moreover, the fast orthogonal search provides a precise extraction of the target's model parameters that may be used for tracking purposes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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