Human Visual System Based Framework for Concealed Weapon Detection
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an efficient concealed weapon detection algorithm is proposed which uses the characteristics of human visual system in frame let domain. The main idea is to decompose the visual and IR/MMW images to be fused into low and high frequency bands using frame let transform. The fusion is performed by two different strategies while exploiting the characteristics of low and high frequency bands. The first strategy is adaptive weighted average based on local energy and is applied to fuse the low-frequency bands. In order to fuse high frequency bands, a new strategy is developed based on texture while exploiting the human visual system characteristics, which can preserve more details from source images and further improve the quality of detected weapons in the fused image. Experimental results demonstrate the efficiency and robustness of the proposed concealed weapon detection algorithm in visual inspection and objective evaluation criteria.
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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 | 0.000 | 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