Target detection and recognition improvements by use of spatiotemporal fusion
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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