Detection of motion-defined form using night vision goggles
Bibliographic record
Abstract
Perception of motion-defined form is important in operational tasks such as search and rescue and camouflage breaking. Previously, we used synthetic Aviator Night Vision Imaging System (ANVIS-9) imagery to demonstrate that the capacity to detect motion-defined form was degraded at low levels of illumination (see Macuda et al., 2004; Thomas et al., 2004). To validate our simulated NVG results, the current study evaluated observer’s ability to detect motion-defined form through a real ANVIS-9 system. The image sequences consisted of a target (square) that moved at a different speed than the background, or only depicted the moving background. For each trial, subjects were shown a pair of image sequences and required to indicate which sequence contained the target stimulus. Mean illumination and hence image noise level was varied by means of Neutral Density (ND) filters placed in front of the NVG objectives. At each noise level, we tested subjects at a series of target speeds. With both real and simulated NVG imagery, subjects had increased difficulty detecting the target with increased noise levels, at both slower and higher target speeds. These degradations in performance should be considered in operational planning. Further research is necessary to expand our understanding of the impact of NVG-produced noise on visual mechanisms.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".