Target detection and recognition improvements by use of spatiotemporal fusion
Bibliographic record
Abstract
We developed spatiotemporal fusion techniques for improving target detection and automatic target recognition. We also investigated real IR (infrared) sensor clutter noise. The sensor noise was collected by an IR (256 x 256) sensor looking at various scenes (trees, grass, roads, buildings, etc.). More than 95% of the sensor pixels showed near-stationary sensor clutter noise that was uncorrelated between pixels as well as across time frames. However, in a few pixels (covering the grass near the road) the sensor noise showed nonstationary properties (with increasing or decreasing mean across time frames). The natural noise extracted from the IR sensor, as well as the computer-generated noise with Gaussian and Rayleigh distributions, was used to test and compare different spatiotemporal fusion strategies. Finally, we proposed two advanced detection schemes: the double-thresholding the reverse-thresholding techniques. These techniques may be applied to complicated clutter situations (e.g., very-high clutter or nonstationary clutter situations) where the traditional constant-false-alarm-ratio technique may fail.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".