Small infrared target detection using two-dimensional fast orthogonal search (2D-FOS)
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".